In [1]:
import pandas as pd
import numpy as np
from datetime import timedelta
import datetime as dt
np.seterr(divide='ignore', invalid='ignore')
import plotly.express as px
import plotly.graph_objects as go
from ipyleaflet import *
import pycountry
In [2]:
#Create reference to CSV file
csv_path = "Resources/H1N1_2009.csv"
csv_path_2 = "Resources/covid_19_data.csv"

#Impor the CSV into a pandas DataFrame
h1n1 = pd.read_csv(csv_path, parse_dates=["Update Time"], encoding = 'unicode_escape')
covid = pd.read_csv(csv_path_2, parse_dates=["ObservationDate"])
In [3]:
#location_df
covid
Out[3]:
SNo ObservationDate Province/State Country/Region Last Update Confirmed Deaths Recovered
0 1 2020-01-22 Anhui Mainland China 1/22/2020 17:00 1.0 0.0 0.0
1 2 2020-01-22 Beijing Mainland China 1/22/2020 17:00 14.0 0.0 0.0
2 3 2020-01-22 Chongqing Mainland China 1/22/2020 17:00 6.0 0.0 0.0
3 4 2020-01-22 Fujian Mainland China 1/22/2020 17:00 1.0 0.0 0.0
4 5 2020-01-22 Gansu Mainland China 1/22/2020 17:00 0.0 0.0 0.0
... ... ... ... ... ... ... ... ...
18641 18642 2020-04-25 Wyoming US 2020-04-26 02:31:18 491.0 7.0 0.0
18642 18643 2020-04-25 Xinjiang Mainland China 2020-04-26 02:31:18 76.0 3.0 73.0
18643 18644 2020-04-25 Yukon Canada 2020-04-26 02:31:18 11.0 0.0 0.0
18644 18645 2020-04-25 Yunnan Mainland China 2020-04-26 02:31:18 185.0 2.0 181.0
18645 18646 2020-04-25 Zhejiang Mainland China 2020-04-26 02:31:18 1268.0 1.0 1257.0

18646 rows × 8 columns

In [4]:
covid = covid.loc[:,['ObservationDate', 'Province/State', 'Country/Region', 'Confirmed', 'Deaths', 'Recovered']]

#Rename Columns
covid = covid.rename(columns={"ObservationDate": "Date"})

covid = covid[['Province/State', 'Country/Region', 'Date', 'Confirmed', 'Deaths', 'Recovered']]

covid
Out[4]:
Province/State Country/Region Date Confirmed Deaths Recovered
0 Anhui Mainland China 2020-01-22 1.0 0.0 0.0
1 Beijing Mainland China 2020-01-22 14.0 0.0 0.0
2 Chongqing Mainland China 2020-01-22 6.0 0.0 0.0
3 Fujian Mainland China 2020-01-22 1.0 0.0 0.0
4 Gansu Mainland China 2020-01-22 0.0 0.0 0.0
... ... ... ... ... ... ...
18641 Wyoming US 2020-04-25 491.0 7.0 0.0
18642 Xinjiang Mainland China 2020-04-25 76.0 3.0 73.0
18643 Yukon Canada 2020-04-25 11.0 0.0 0.0
18644 Yunnan Mainland China 2020-04-25 185.0 2.0 181.0
18645 Zhejiang Mainland China 2020-04-25 1268.0 1.0 1257.0

18646 rows × 6 columns

In [5]:
temp = covid

#Group Provinces and take largest cumulative confirmed and death number
province_df = temp.groupby(by='Province/State').agg('max').reset_index(drop=False)

#Group all provinces into their countries and add confirmed and death numbers
province_df = province_df.groupby(by='Country/Region').agg('sum').reset_index(drop=False)


province_df
Out[5]:
Country/Region Confirmed Deaths Recovered
0 Australia 6694.0 80.0 5271.0
1 Canada 46357.0 2565.0 14.0
2 Denmark 1524.0 13.0 190.0
3 France 35456.0 1456.0 2949.0
4 Germany 5.0 0.0 0.0
5 Hong Kong 1037.0 4.0 753.0
6 Israel 8.0 0.0 0.0
7 Lebanon 2.0 0.0 0.0
8 Macau 45.0 0.0 28.0
9 Mainland China 82827.0 4632.0 78225.0
10 Netherlands 3825.0 151.0 104.0
11 Others 61.0 0.0 0.0
12 Taiwan 47.0 1.0 17.0
13 UK 6668.0 303.0 857.0
14 US 939634.0 53786.0 101141.0
In [6]:
#Confirmed, Deaths and Recovered are cumulative numbers per Province/State NOT by Country/Region

#Remove countries that are in province_df dataset
remove_list = province_df['Country/Region']
country_data = temp[~temp['Country/Region'].isin(remove_list)]

#province_df
country_data = country_data.loc[:,['Country/Region', 'Date', 'Confirmed', 'Deaths', 'Recovered']]
country_data = country_data.reset_index(drop=True)
country_data
Out[6]:
Country/Region Date Confirmed Deaths Recovered
0 Japan 2020-01-22 2.0 0.0 0.0
1 Thailand 2020-01-22 2.0 0.0 0.0
2 South Korea 2020-01-22 1.0 0.0 0.0
3 Japan 2020-01-23 1.0 0.0 0.0
4 Thailand 2020-01-23 3.0 0.0 0.0
... ... ... ... ... ...
8940 West Bank and Gaza 2020-04-25 342.0 2.0 92.0
8941 Western Sahara 2020-04-25 6.0 0.0 5.0
8942 Yemen 2020-04-25 1.0 0.0 1.0
8943 Zambia 2020-04-25 84.0 3.0 37.0
8944 Zimbabwe 2020-04-25 31.0 4.0 2.0

8945 rows × 5 columns

In [7]:
#Merge province and country data
complete_data = pd.concat([country_data, province_df], ignore_index=True)
In [8]:
complete_data
Out[8]:
Country/Region Date Confirmed Deaths Recovered
0 Japan 2020-01-22 2.0 0.0 0.0
1 Thailand 2020-01-22 2.0 0.0 0.0
2 South Korea 2020-01-22 1.0 0.0 0.0
3 Japan 2020-01-23 1.0 0.0 0.0
4 Thailand 2020-01-23 3.0 0.0 0.0
... ... ... ... ... ...
8955 Netherlands NaT 3825.0 151.0 104.0
8956 Others NaT 61.0 0.0 0.0
8957 Taiwan NaT 47.0 1.0 17.0
8958 UK NaT 6668.0 303.0 857.0
8959 US NaT 939634.0 53786.0 101141.0

8960 rows × 5 columns

In [9]:
covid_2 = pd.read_csv(csv_path_2, parse_dates=["ObservationDate"])

covid_2 = covid_2.loc[:,['ObservationDate', 'Province/State', 'Country/Region', 'Confirmed', 'Deaths', 'Recovered']]

#Rename Columns
covid_2 = covid_2.rename(columns={"ObservationDate": "Date", "Province/State" : "Province", "Country/Region" : "Country"})

covid_2 = covid_2.groupby(['Date', 'Country'])[["Confirmed", "Deaths", "Recovered"]].sum().reset_index()

covid_2
Out[9]:
Date Country Confirmed Deaths Recovered
0 2020-01-22 Hong Kong 0.0 0.0 0.0
1 2020-01-22 Japan 2.0 0.0 0.0
2 2020-01-22 Macau 1.0 0.0 0.0
3 2020-01-22 Mainland China 547.0 17.0 28.0
4 2020-01-22 South Korea 1.0 0.0 0.0
... ... ... ... ... ...
10161 2020-04-25 West Bank and Gaza 342.0 2.0 92.0
10162 2020-04-25 Western Sahara 6.0 0.0 5.0
10163 2020-04-25 Yemen 1.0 0.0 1.0
10164 2020-04-25 Zambia 84.0 3.0 37.0
10165 2020-04-25 Zimbabwe 31.0 4.0 2.0

10166 rows × 5 columns

Days Since 1st Case & Weeks Since 1st Case

Infected Countries Ordered by Date Since 1st Confirmed Case

In [10]:
#Create Dataset that lists all countries
dfww = covid.loc[:,['Country/Region', 'Date', 'Confirmed', 'Deaths', 'Recovered']]

#Create DataFrame of days with Confirmed Cases
df1 = dfww[dfww['Confirmed']>0]
df1 = df1.reset_index(drop=True)

#Find start date of infections in YYY-DD-MM format
start_day = df1.groupby(by='Country/Region').agg('min').reset_index(drop=False)
start_day = start_day.loc[:,['Country/Region', 'Date']]

#Find todays date in YYYY-DD-MM format
today = dt.datetime.today()

#Days since first infection date
days_difference = []
days_difference = (today - start_day['Date']).dt.days
days_difference

#Merge on index
start_day = start_day.merge(days_difference, left_index=True, right_index=True)
start_day = start_day.rename(columns={'Country/Region': 'Country/Region', 
                                      'Date_x': 'Start Date', 
                                      'Date_y': 'Days Since 1st Case'})

start_day['Weeks Since 1st Case'] = round(start_day['Days Since 1st Case'] / 7, 2)

start_day.sort_values(by='Days Since 1st Case', ascending=False)\
.reset_index(drop=True).style.background_gradient(cmap='Reds')
#start_day
Out[10]:
Country/Region Start Date Days Since 1st Case Weeks Since 1st Case
0 Taiwan 2020-01-22 00:00:00 95 13.570000
1 Macau 2020-01-22 00:00:00 95 13.570000
2 Mainland China 2020-01-22 00:00:00 95 13.570000
3 Japan 2020-01-22 00:00:00 95 13.570000
4 Thailand 2020-01-22 00:00:00 95 13.570000
5 South Korea 2020-01-22 00:00:00 95 13.570000
6 US 2020-01-22 00:00:00 95 13.570000
7 Singapore 2020-01-23 00:00:00 94 13.430000
8 Vietnam 2020-01-23 00:00:00 94 13.430000
9 Hong Kong 2020-01-23 00:00:00 94 13.430000
10 France 2020-01-24 00:00:00 93 13.290000
11 Australia 2020-01-25 00:00:00 92 13.140000
12 Nepal 2020-01-25 00:00:00 92 13.140000
13 Malaysia 2020-01-25 00:00:00 92 13.140000
14 Canada 2020-01-26 00:00:00 91 13.000000
15 Cambodia 2020-01-27 00:00:00 90 12.860000
16 Sri Lanka 2020-01-27 00:00:00 90 12.860000
17 Ivory Coast 2020-01-27 00:00:00 90 12.860000
18 Germany 2020-01-28 00:00:00 89 12.710000
19 United Arab Emirates 2020-01-29 00:00:00 88 12.570000
20 Finland 2020-01-29 00:00:00 88 12.570000
21 India 2020-01-30 00:00:00 87 12.430000
22 Philippines 2020-01-30 00:00:00 87 12.430000
23 Sweden 2020-01-31 00:00:00 86 12.290000
24 Russia 2020-01-31 00:00:00 86 12.290000
25 UK 2020-01-31 00:00:00 86 12.290000
26 Italy 2020-01-31 00:00:00 86 12.290000
27 Spain 2020-02-01 00:00:00 85 12.140000
28 Belgium 2020-02-04 00:00:00 82 11.710000
29 Others 2020-02-07 00:00:00 79 11.290000
30 Egypt 2020-02-14 00:00:00 72 10.290000
31 Iran 2020-02-19 00:00:00 67 9.570000
32 Lebanon 2020-02-21 00:00:00 65 9.290000
33 Israel 2020-02-21 00:00:00 65 9.290000
34 Iraq 2020-02-24 00:00:00 62 8.860000
35 Afghanistan 2020-02-24 00:00:00 62 8.860000
36 Bahrain 2020-02-24 00:00:00 62 8.860000
37 Oman 2020-02-24 00:00:00 62 8.860000
38 Kuwait 2020-02-24 00:00:00 62 8.860000
39 Croatia 2020-02-25 00:00:00 61 8.710000
40 Algeria 2020-02-25 00:00:00 61 8.710000
41 Switzerland 2020-02-25 00:00:00 61 8.710000
42 Austria 2020-02-25 00:00:00 61 8.710000
43 Georgia 2020-02-26 00:00:00 60 8.570000
44 Brazil 2020-02-26 00:00:00 60 8.570000
45 North Macedonia 2020-02-26 00:00:00 60 8.570000
46 Norway 2020-02-26 00:00:00 60 8.570000
47 Romania 2020-02-26 00:00:00 60 8.570000
48 Pakistan 2020-02-26 00:00:00 60 8.570000
49 Greece 2020-02-26 00:00:00 60 8.570000
50 San Marino 2020-02-27 00:00:00 59 8.430000
51 Estonia 2020-02-27 00:00:00 59 8.430000
52 Denmark 2020-02-27 00:00:00 59 8.430000
53 Netherlands 2020-02-27 00:00:00 59 8.430000
54 Lithuania 2020-02-28 00:00:00 58 8.290000
55 Azerbaijan 2020-02-28 00:00:00 58 8.290000
56 Belarus 2020-02-28 00:00:00 58 8.290000
57 Iceland 2020-02-28 00:00:00 58 8.290000
58 Mexico 2020-02-28 00:00:00 58 8.290000
59 North Ireland 2020-02-28 00:00:00 58 8.290000
60 Nigeria 2020-02-28 00:00:00 58 8.290000
61 New Zealand 2020-02-28 00:00:00 58 8.290000
62 Luxembourg 2020-02-29 00:00:00 57 8.140000
63 Qatar 2020-02-29 00:00:00 57 8.140000
64 Ireland 2020-02-29 00:00:00 57 8.140000
65 Monaco 2020-02-29 00:00:00 57 8.140000
66 Ecuador 2020-03-01 00:00:00 56 8.000000
67 Dominican Republic 2020-03-01 00:00:00 56 8.000000
68 Czech Republic 2020-03-01 00:00:00 56 8.000000
69 Armenia 2020-03-01 00:00:00 56 8.000000
70 Azerbaijan 2020-03-01 00:00:00 56 8.000000
71 Senegal 2020-03-02 00:00:00 55 7.860000
72 Saudi Arabia 2020-03-02 00:00:00 55 7.860000
73 Indonesia 2020-03-02 00:00:00 55 7.860000
74 Morocco 2020-03-02 00:00:00 55 7.860000
75 Portugal 2020-03-02 00:00:00 55 7.860000
76 Latvia 2020-03-02 00:00:00 55 7.860000
77 Andorra 2020-03-02 00:00:00 55 7.860000
78 Jordan 2020-03-03 00:00:00 54 7.710000
79 Chile 2020-03-03 00:00:00 54 7.710000
80 Ukraine 2020-03-03 00:00:00 54 7.710000
81 Argentina 2020-03-03 00:00:00 54 7.710000
82 Tunisia 2020-03-04 00:00:00 53 7.570000
83 Faroe Islands 2020-03-04 00:00:00 53 7.570000
84 Hungary 2020-03-04 00:00:00 53 7.570000
85 Liechtenstein 2020-03-04 00:00:00 53 7.570000
86 Saint Barthelemy 2020-03-04 00:00:00 53 7.570000
87 Poland 2020-03-04 00:00:00 53 7.570000
88 Gibraltar 2020-03-04 00:00:00 53 7.570000
89 Bosnia and Herzegovina 2020-03-05 00:00:00 52 7.430000
90 Slovenia 2020-03-05 00:00:00 52 7.430000
91 South Africa 2020-03-05 00:00:00 52 7.430000
92 Palestine 2020-03-05 00:00:00 52 7.430000
93 Vatican City 2020-03-06 00:00:00 51 7.290000
94 Slovakia 2020-03-06 00:00:00 51 7.290000
95 Togo 2020-03-06 00:00:00 51 7.290000
96 Bhutan 2020-03-06 00:00:00 51 7.290000
97 Colombia 2020-03-06 00:00:00 51 7.290000
98 Peru 2020-03-06 00:00:00 51 7.290000
99 Cameroon 2020-03-06 00:00:00 51 7.290000
100 Serbia 2020-03-06 00:00:00 51 7.290000
101 Costa Rica 2020-03-06 00:00:00 51 7.290000
102 French Guiana 2020-03-07 00:00:00 50 7.140000
103 Martinique 2020-03-07 00:00:00 50 7.140000
104 Malta 2020-03-07 00:00:00 50 7.140000
105 Republic of Ireland 2020-03-08 00:00:00 49 7.000000
106 Bangladesh 2020-03-08 00:00:00 49 7.000000
107 Bulgaria 2020-03-08 00:00:00 49 7.000000
108 Maldives 2020-03-08 00:00:00 49 7.000000
109 Moldova 2020-03-08 00:00:00 49 7.000000
110 Paraguay 2020-03-08 00:00:00 49 7.000000
111 St. Martin 2020-03-09 00:00:00 48 6.860000
112 Cyprus 2020-03-09 00:00:00 48 6.860000
113 Albania 2020-03-09 00:00:00 48 6.860000
114 Brunei 2020-03-09 00:00:00 48 6.860000
115 Panama 2020-03-10 00:00:00 47 6.710000
116 Mongolia 2020-03-10 00:00:00 47 6.710000
117 occupied Palestinian territory 2020-03-10 00:00:00 47 6.710000
118 Burkina Faso 2020-03-10 00:00:00 47 6.710000
119 ('St. Martin',) 2020-03-10 00:00:00 47 6.710000
120 Channel Islands 2020-03-10 00:00:00 47 6.710000
121 Holy See 2020-03-10 00:00:00 47 6.710000
122 Honduras 2020-03-11 00:00:00 46 6.570000
123 Congo (Kinshasa) 2020-03-11 00:00:00 46 6.570000
124 Turkey 2020-03-11 00:00:00 46 6.570000
125 Bolivia 2020-03-11 00:00:00 46 6.570000
126 Reunion 2020-03-11 00:00:00 46 6.570000
127 Jamaica 2020-03-11 00:00:00 46 6.570000
128 Cuba 2020-03-12 00:00:00 45 6.430000
129 Guyana 2020-03-12 00:00:00 45 6.430000
130 Aruba 2020-03-13 00:00:00 44 6.290000
131 Cayman Islands 2020-03-13 00:00:00 44 6.290000
132 Guinea 2020-03-13 00:00:00 44 6.290000
133 Ethiopia 2020-03-13 00:00:00 44 6.290000
134 Sudan 2020-03-13 00:00:00 44 6.290000
135 Guadeloupe 2020-03-13 00:00:00 44 6.290000
136 Kazakhstan 2020-03-13 00:00:00 44 6.290000
137 Kenya 2020-03-13 00:00:00 44 6.290000
138 Antigua and Barbuda 2020-03-13 00:00:00 44 6.290000
139 Seychelles 2020-03-14 00:00:00 43 6.140000
140 Eswatini 2020-03-14 00:00:00 43 6.140000
141 Rwanda 2020-03-14 00:00:00 43 6.140000
142 Gabon 2020-03-14 00:00:00 43 6.140000
143 Saint Lucia 2020-03-14 00:00:00 43 6.140000
144 Saint Vincent and the Grenadines 2020-03-14 00:00:00 43 6.140000
145 Ghana 2020-03-14 00:00:00 43 6.140000
146 Guatemala 2020-03-14 00:00:00 43 6.140000
147 Uruguay 2020-03-14 00:00:00 43 6.140000
148 Mauritania 2020-03-14 00:00:00 43 6.140000
149 Guernsey 2020-03-14 00:00:00 43 6.140000
150 Suriname 2020-03-14 00:00:00 43 6.140000
151 Venezuela 2020-03-14 00:00:00 43 6.140000
152 Namibia 2020-03-14 00:00:00 43 6.140000
153 Trinidad and Tobago 2020-03-14 00:00:00 43 6.140000
154 Curacao 2020-03-14 00:00:00 43 6.140000
155 Jersey 2020-03-14 00:00:00 43 6.140000
156 Equatorial Guinea 2020-03-15 00:00:00 42 6.000000
157 Uzbekistan 2020-03-15 00:00:00 42 6.000000
158 Central African Republic 2020-03-15 00:00:00 42 6.000000
159 Congo (Brazzaville) 2020-03-15 00:00:00 42 6.000000
160 Kosovo 2020-03-15 00:00:00 42 6.000000
161 Liberia 2020-03-16 00:00:00 41 5.860000
162 Benin 2020-03-16 00:00:00 41 5.860000
163 The Bahamas 2020-03-16 00:00:00 41 5.860000
164 Guam 2020-03-16 00:00:00 41 5.860000
165 Mayotte 2020-03-16 00:00:00 41 5.860000
166 Republic of the Congo 2020-03-16 00:00:00 41 5.860000
167 Tanzania 2020-03-16 00:00:00 41 5.860000
168 Somalia 2020-03-16 00:00:00 41 5.860000
169 Greenland 2020-03-16 00:00:00 41 5.860000
170 Puerto Rico 2020-03-16 00:00:00 41 5.860000
171 Montenegro 2020-03-17 00:00:00 40 5.710000
172 The Gambia 2020-03-17 00:00:00 40 5.710000
173 Barbados 2020-03-17 00:00:00 40 5.710000
174 Kyrgyzstan 2020-03-18 00:00:00 39 5.570000
175 Djibouti 2020-03-18 00:00:00 39 5.570000
176 Zambia 2020-03-18 00:00:00 39 5.570000
177 Gambia, The 2020-03-18 00:00:00 39 5.570000
178 Mauritius 2020-03-18 00:00:00 39 5.570000
179 El Salvador 2020-03-19 00:00:00 38 5.430000
180 Fiji 2020-03-19 00:00:00 38 5.430000
181 Chad 2020-03-19 00:00:00 38 5.430000
182 Nicaragua 2020-03-19 00:00:00 38 5.430000
183 Bahamas, The 2020-03-19 00:00:00 38 5.430000
184 Cabo Verde 2020-03-20 00:00:00 37 5.290000
185 Angola 2020-03-20 00:00:00 37 5.290000
186 Niger 2020-03-20 00:00:00 37 5.290000
187 Zimbabwe 2020-03-20 00:00:00 37 5.290000
188 Madagascar 2020-03-20 00:00:00 37 5.290000
189 Papua New Guinea 2020-03-20 00:00:00 37 5.290000
190 Haiti 2020-03-20 00:00:00 37 5.290000
191 Uganda 2020-03-21 00:00:00 36 5.140000
192 Cape Verde 2020-03-21 00:00:00 36 5.140000
193 Eritrea 2020-03-21 00:00:00 36 5.140000
194 East Timor 2020-03-21 00:00:00 36 5.140000
195 Timor-Leste 2020-03-22 00:00:00 35 5.000000
196 Grenada 2020-03-22 00:00:00 35 5.000000
197 Mozambique 2020-03-22 00:00:00 35 5.000000
198 Syria 2020-03-22 00:00:00 35 5.000000
199 Dominica 2020-03-22 00:00:00 35 5.000000
200 Bahamas 2020-03-22 00:00:00 35 5.000000
201 Gambia 2020-03-22 00:00:00 35 5.000000
202 Belize 2020-03-23 00:00:00 34 4.860000
203 Libya 2020-03-24 00:00:00 33 4.710000
204 Laos 2020-03-24 00:00:00 33 4.710000
205 Diamond Princess 2020-03-25 00:00:00 32 4.570000
206 Guinea-Bissau 2020-03-25 00:00:00 32 4.570000
207 Mali 2020-03-25 00:00:00 32 4.570000
208 Saint Kitts and Nevis 2020-03-25 00:00:00 32 4.570000
209 West Bank and Gaza 2020-03-26 00:00:00 31 4.430000
210 Burma 2020-03-27 00:00:00 30 4.290000
211 MS Zaandam 2020-03-28 00:00:00 29 4.140000
212 Botswana 2020-03-30 00:00:00 27 3.860000
213 Burundi 2020-03-31 00:00:00 26 3.710000
214 Sierra Leone 2020-03-31 00:00:00 26 3.710000
215 Malawi 2020-04-02 00:00:00 24 3.430000
216 South Sudan 2020-04-05 00:00:00 21 3.000000
217 Western Sahara 2020-04-05 00:00:00 21 3.000000
218 Sao Tome and Principe 2020-04-06 00:00:00 20 2.860000
219 Yemen 2020-04-10 00:00:00 16 2.290000
In [11]:
start_day.sort_values(by='Days Since 1st Case', ascending=True)\
.reset_index(drop=True).style.background_gradient(cmap='Reds')
Out[11]:
Country/Region Start Date Days Since 1st Case Weeks Since 1st Case
0 Yemen 2020-04-10 00:00:00 16 2.290000
1 Sao Tome and Principe 2020-04-06 00:00:00 20 2.860000
2 South Sudan 2020-04-05 00:00:00 21 3.000000
3 Western Sahara 2020-04-05 00:00:00 21 3.000000
4 Malawi 2020-04-02 00:00:00 24 3.430000
5 Sierra Leone 2020-03-31 00:00:00 26 3.710000
6 Burundi 2020-03-31 00:00:00 26 3.710000
7 Botswana 2020-03-30 00:00:00 27 3.860000
8 MS Zaandam 2020-03-28 00:00:00 29 4.140000
9 Burma 2020-03-27 00:00:00 30 4.290000
10 West Bank and Gaza 2020-03-26 00:00:00 31 4.430000
11 Mali 2020-03-25 00:00:00 32 4.570000
12 Saint Kitts and Nevis 2020-03-25 00:00:00 32 4.570000
13 Diamond Princess 2020-03-25 00:00:00 32 4.570000
14 Guinea-Bissau 2020-03-25 00:00:00 32 4.570000
15 Libya 2020-03-24 00:00:00 33 4.710000
16 Laos 2020-03-24 00:00:00 33 4.710000
17 Belize 2020-03-23 00:00:00 34 4.860000
18 Bahamas 2020-03-22 00:00:00 35 5.000000
19 Dominica 2020-03-22 00:00:00 35 5.000000
20 Timor-Leste 2020-03-22 00:00:00 35 5.000000
21 Mozambique 2020-03-22 00:00:00 35 5.000000
22 Gambia 2020-03-22 00:00:00 35 5.000000
23 Syria 2020-03-22 00:00:00 35 5.000000
24 Grenada 2020-03-22 00:00:00 35 5.000000
25 East Timor 2020-03-21 00:00:00 36 5.140000
26 Eritrea 2020-03-21 00:00:00 36 5.140000
27 Uganda 2020-03-21 00:00:00 36 5.140000
28 Cape Verde 2020-03-21 00:00:00 36 5.140000
29 Cabo Verde 2020-03-20 00:00:00 37 5.290000
30 Papua New Guinea 2020-03-20 00:00:00 37 5.290000
31 Madagascar 2020-03-20 00:00:00 37 5.290000
32 Angola 2020-03-20 00:00:00 37 5.290000
33 Haiti 2020-03-20 00:00:00 37 5.290000
34 Zimbabwe 2020-03-20 00:00:00 37 5.290000
35 Niger 2020-03-20 00:00:00 37 5.290000
36 Bahamas, The 2020-03-19 00:00:00 38 5.430000
37 El Salvador 2020-03-19 00:00:00 38 5.430000
38 Chad 2020-03-19 00:00:00 38 5.430000
39 Fiji 2020-03-19 00:00:00 38 5.430000
40 Nicaragua 2020-03-19 00:00:00 38 5.430000
41 Mauritius 2020-03-18 00:00:00 39 5.570000
42 Kyrgyzstan 2020-03-18 00:00:00 39 5.570000
43 Djibouti 2020-03-18 00:00:00 39 5.570000
44 Zambia 2020-03-18 00:00:00 39 5.570000
45 Gambia, The 2020-03-18 00:00:00 39 5.570000
46 The Gambia 2020-03-17 00:00:00 40 5.710000
47 Barbados 2020-03-17 00:00:00 40 5.710000
48 Montenegro 2020-03-17 00:00:00 40 5.710000
49 Greenland 2020-03-16 00:00:00 41 5.860000
50 Guam 2020-03-16 00:00:00 41 5.860000
51 Tanzania 2020-03-16 00:00:00 41 5.860000
52 Liberia 2020-03-16 00:00:00 41 5.860000
53 The Bahamas 2020-03-16 00:00:00 41 5.860000
54 Somalia 2020-03-16 00:00:00 41 5.860000
55 Puerto Rico 2020-03-16 00:00:00 41 5.860000
56 Republic of the Congo 2020-03-16 00:00:00 41 5.860000
57 Benin 2020-03-16 00:00:00 41 5.860000
58 Mayotte 2020-03-16 00:00:00 41 5.860000
59 Uzbekistan 2020-03-15 00:00:00 42 6.000000
60 Equatorial Guinea 2020-03-15 00:00:00 42 6.000000
61 Central African Republic 2020-03-15 00:00:00 42 6.000000
62 Congo (Brazzaville) 2020-03-15 00:00:00 42 6.000000
63 Kosovo 2020-03-15 00:00:00 42 6.000000
64 Suriname 2020-03-14 00:00:00 43 6.140000
65 Jersey 2020-03-14 00:00:00 43 6.140000
66 Guernsey 2020-03-14 00:00:00 43 6.140000
67 Guatemala 2020-03-14 00:00:00 43 6.140000
68 Rwanda 2020-03-14 00:00:00 43 6.140000
69 Venezuela 2020-03-14 00:00:00 43 6.140000
70 Ghana 2020-03-14 00:00:00 43 6.140000
71 Saint Lucia 2020-03-14 00:00:00 43 6.140000
72 Curacao 2020-03-14 00:00:00 43 6.140000
73 Gabon 2020-03-14 00:00:00 43 6.140000
74 Trinidad and Tobago 2020-03-14 00:00:00 43 6.140000
75 Mauritania 2020-03-14 00:00:00 43 6.140000
76 Uruguay 2020-03-14 00:00:00 43 6.140000
77 Seychelles 2020-03-14 00:00:00 43 6.140000
78 Eswatini 2020-03-14 00:00:00 43 6.140000
79 Namibia 2020-03-14 00:00:00 43 6.140000
80 Saint Vincent and the Grenadines 2020-03-14 00:00:00 43 6.140000
81 Kazakhstan 2020-03-13 00:00:00 44 6.290000
82 Sudan 2020-03-13 00:00:00 44 6.290000
83 Kenya 2020-03-13 00:00:00 44 6.290000
84 Antigua and Barbuda 2020-03-13 00:00:00 44 6.290000
85 Cayman Islands 2020-03-13 00:00:00 44 6.290000
86 Ethiopia 2020-03-13 00:00:00 44 6.290000
87 Guadeloupe 2020-03-13 00:00:00 44 6.290000
88 Aruba 2020-03-13 00:00:00 44 6.290000
89 Guinea 2020-03-13 00:00:00 44 6.290000
90 Guyana 2020-03-12 00:00:00 45 6.430000
91 Cuba 2020-03-12 00:00:00 45 6.430000
92 Jamaica 2020-03-11 00:00:00 46 6.570000
93 Honduras 2020-03-11 00:00:00 46 6.570000
94 Bolivia 2020-03-11 00:00:00 46 6.570000
95 Reunion 2020-03-11 00:00:00 46 6.570000
96 Congo (Kinshasa) 2020-03-11 00:00:00 46 6.570000
97 Turkey 2020-03-11 00:00:00 46 6.570000
98 Mongolia 2020-03-10 00:00:00 47 6.710000
99 Panama 2020-03-10 00:00:00 47 6.710000
100 occupied Palestinian territory 2020-03-10 00:00:00 47 6.710000
101 ('St. Martin',) 2020-03-10 00:00:00 47 6.710000
102 Holy See 2020-03-10 00:00:00 47 6.710000
103 Burkina Faso 2020-03-10 00:00:00 47 6.710000
104 Channel Islands 2020-03-10 00:00:00 47 6.710000
105 Cyprus 2020-03-09 00:00:00 48 6.860000
106 Albania 2020-03-09 00:00:00 48 6.860000
107 St. Martin 2020-03-09 00:00:00 48 6.860000
108 Brunei 2020-03-09 00:00:00 48 6.860000
109 Paraguay 2020-03-08 00:00:00 49 7.000000
110 Republic of Ireland 2020-03-08 00:00:00 49 7.000000
111 Bangladesh 2020-03-08 00:00:00 49 7.000000
112 Maldives 2020-03-08 00:00:00 49 7.000000
113 Bulgaria 2020-03-08 00:00:00 49 7.000000
114 Moldova 2020-03-08 00:00:00 49 7.000000
115 Malta 2020-03-07 00:00:00 50 7.140000
116 Martinique 2020-03-07 00:00:00 50 7.140000
117 French Guiana 2020-03-07 00:00:00 50 7.140000
118 Bhutan 2020-03-06 00:00:00 51 7.290000
119 Vatican City 2020-03-06 00:00:00 51 7.290000
120 Costa Rica 2020-03-06 00:00:00 51 7.290000
121 Cameroon 2020-03-06 00:00:00 51 7.290000
122 Slovakia 2020-03-06 00:00:00 51 7.290000
123 Colombia 2020-03-06 00:00:00 51 7.290000
124 Peru 2020-03-06 00:00:00 51 7.290000
125 Serbia 2020-03-06 00:00:00 51 7.290000
126 Togo 2020-03-06 00:00:00 51 7.290000
127 Bosnia and Herzegovina 2020-03-05 00:00:00 52 7.430000
128 South Africa 2020-03-05 00:00:00 52 7.430000
129 Palestine 2020-03-05 00:00:00 52 7.430000
130 Slovenia 2020-03-05 00:00:00 52 7.430000
131 Hungary 2020-03-04 00:00:00 53 7.570000
132 Saint Barthelemy 2020-03-04 00:00:00 53 7.570000
133 Gibraltar 2020-03-04 00:00:00 53 7.570000
134 Faroe Islands 2020-03-04 00:00:00 53 7.570000
135 Poland 2020-03-04 00:00:00 53 7.570000
136 Tunisia 2020-03-04 00:00:00 53 7.570000
137 Liechtenstein 2020-03-04 00:00:00 53 7.570000
138 Chile 2020-03-03 00:00:00 54 7.710000
139 Ukraine 2020-03-03 00:00:00 54 7.710000
140 Argentina 2020-03-03 00:00:00 54 7.710000
141 Jordan 2020-03-03 00:00:00 54 7.710000
142 Saudi Arabia 2020-03-02 00:00:00 55 7.860000
143 Morocco 2020-03-02 00:00:00 55 7.860000
144 Latvia 2020-03-02 00:00:00 55 7.860000
145 Indonesia 2020-03-02 00:00:00 55 7.860000
146 Senegal 2020-03-02 00:00:00 55 7.860000
147 Portugal 2020-03-02 00:00:00 55 7.860000
148 Andorra 2020-03-02 00:00:00 55 7.860000
149 Armenia 2020-03-01 00:00:00 56 8.000000
150 Azerbaijan 2020-03-01 00:00:00 56 8.000000
151 Dominican Republic 2020-03-01 00:00:00 56 8.000000
152 Czech Republic 2020-03-01 00:00:00 56 8.000000
153 Ecuador 2020-03-01 00:00:00 56 8.000000
154 Luxembourg 2020-02-29 00:00:00 57 8.140000
155 Ireland 2020-02-29 00:00:00 57 8.140000
156 Qatar 2020-02-29 00:00:00 57 8.140000
157 Monaco 2020-02-29 00:00:00 57 8.140000
158 Azerbaijan 2020-02-28 00:00:00 58 8.290000
159 Iceland 2020-02-28 00:00:00 58 8.290000
160 Belarus 2020-02-28 00:00:00 58 8.290000
161 North Ireland 2020-02-28 00:00:00 58 8.290000
162 Nigeria 2020-02-28 00:00:00 58 8.290000
163 New Zealand 2020-02-28 00:00:00 58 8.290000
164 Lithuania 2020-02-28 00:00:00 58 8.290000
165 Mexico 2020-02-28 00:00:00 58 8.290000
166 Denmark 2020-02-27 00:00:00 59 8.430000
167 Estonia 2020-02-27 00:00:00 59 8.430000
168 Netherlands 2020-02-27 00:00:00 59 8.430000
169 San Marino 2020-02-27 00:00:00 59 8.430000
170 Brazil 2020-02-26 00:00:00 60 8.570000
171 Georgia 2020-02-26 00:00:00 60 8.570000
172 Greece 2020-02-26 00:00:00 60 8.570000
173 Pakistan 2020-02-26 00:00:00 60 8.570000
174 Norway 2020-02-26 00:00:00 60 8.570000
175 North Macedonia 2020-02-26 00:00:00 60 8.570000
176 Romania 2020-02-26 00:00:00 60 8.570000
177 Croatia 2020-02-25 00:00:00 61 8.710000
178 Algeria 2020-02-25 00:00:00 61 8.710000
179 Austria 2020-02-25 00:00:00 61 8.710000
180 Switzerland 2020-02-25 00:00:00 61 8.710000
181 Kuwait 2020-02-24 00:00:00 62 8.860000
182 Oman 2020-02-24 00:00:00 62 8.860000
183 Bahrain 2020-02-24 00:00:00 62 8.860000
184 Iraq 2020-02-24 00:00:00 62 8.860000
185 Afghanistan 2020-02-24 00:00:00 62 8.860000
186 Lebanon 2020-02-21 00:00:00 65 9.290000
187 Israel 2020-02-21 00:00:00 65 9.290000
188 Iran 2020-02-19 00:00:00 67 9.570000
189 Egypt 2020-02-14 00:00:00 72 10.290000
190 Others 2020-02-07 00:00:00 79 11.290000
191 Belgium 2020-02-04 00:00:00 82 11.710000
192 Spain 2020-02-01 00:00:00 85 12.140000
193 Russia 2020-01-31 00:00:00 86 12.290000
194 Italy 2020-01-31 00:00:00 86 12.290000
195 Sweden 2020-01-31 00:00:00 86 12.290000
196 UK 2020-01-31 00:00:00 86 12.290000
197 Philippines 2020-01-30 00:00:00 87 12.430000
198 India 2020-01-30 00:00:00 87 12.430000
199 Finland 2020-01-29 00:00:00 88 12.570000
200 United Arab Emirates 2020-01-29 00:00:00 88 12.570000
201 Germany 2020-01-28 00:00:00 89 12.710000
202 Ivory Coast 2020-01-27 00:00:00 90 12.860000
203 Sri Lanka 2020-01-27 00:00:00 90 12.860000
204 Cambodia 2020-01-27 00:00:00 90 12.860000
205 Canada 2020-01-26 00:00:00 91 13.000000
206 Australia 2020-01-25 00:00:00 92 13.140000
207 Nepal 2020-01-25 00:00:00 92 13.140000
208 Malaysia 2020-01-25 00:00:00 92 13.140000
209 France 2020-01-24 00:00:00 93 13.290000
210 Singapore 2020-01-23 00:00:00 94 13.430000
211 Hong Kong 2020-01-23 00:00:00 94 13.430000
212 Vietnam 2020-01-23 00:00:00 94 13.430000
213 Mainland China 2020-01-22 00:00:00 95 13.570000
214 Taiwan 2020-01-22 00:00:00 95 13.570000
215 South Korea 2020-01-22 00:00:00 95 13.570000
216 Japan 2020-01-22 00:00:00 95 13.570000
217 Macau 2020-01-22 00:00:00 95 13.570000
218 US 2020-01-22 00:00:00 95 13.570000
219 Thailand 2020-01-22 00:00:00 95 13.570000
In [12]:
#Store Country, Days Since 1st Case and Weeks Since 1st Case
start_day = start_day.loc[:, ['Country/Region', 'Days Since 1st Case', 'Weeks Since 1st Case']]
start_day
Out[12]:
Country/Region Days Since 1st Case Weeks Since 1st Case
0 Azerbaijan 58 8.29
1 ('St. Martin',) 47 6.71
2 Afghanistan 62 8.86
3 Albania 48 6.86
4 Algeria 61 8.71
... ... ... ...
215 Western Sahara 21 3.00
216 Yemen 16 2.29
217 Zambia 39 5.57
218 Zimbabwe 37 5.29
219 occupied Palestinian territory 47 6.71

220 rows × 3 columns

Country Comparisons

Confirmed Cases desc. by Country

In [13]:
#Merge datasets together so age metrics follow global analysis
df5 = complete_data.groupby(by='Country/Region').agg('max').reset_index(drop=False)

df5 = pd.merge(df5, start_day, on='Country/Region')
In [14]:
#Add Column for "Active" = 'Confirmed' - 'Deaths' - 'Recovered'

active = df5['Confirmed'] - df5['Deaths'] - df5['Recovered']

df5['Active'] = active

df5 = df5[['Country/Region', 'Date', 
             'Confirmed', 'Deaths', 
             'Recovered', 'Active',
             'Date', 'Days Since 1st Case', 
             'Weeks Since 1st Case'
            ]]

df5 = df5.loc[:,['Country/Region', 'Confirmed', 'Deaths', 'Recovered', 'Active', 'Days Since 1st Case', 'Weeks Since 1st Case']]
df5
Out[14]:
Country/Region Confirmed Deaths Recovered Active Days Since 1st Case Weeks Since 1st Case
0 Azerbaijan 1.0 0.0 0.0 1.0 58 8.29
1 ('St. Martin',) 2.0 0.0 0.0 2.0 47 6.71
2 Afghanistan 1463.0 47.0 188.0 1228.0 62 8.86
3 Albania 712.0 27.0 403.0 282.0 48 6.86
4 Algeria 3256.0 419.0 1479.0 1358.0 61 8.71
... ... ... ... ... ... ... ...
215 Western Sahara 6.0 0.0 5.0 1.0 21 3.00
216 Yemen 1.0 0.0 1.0 0.0 16 2.29
217 Zambia 84.0 3.0 37.0 44.0 39 5.57
218 Zimbabwe 31.0 4.0 2.0 25.0 37 5.29
219 occupied Palestinian territory 25.0 0.0 0.0 25.0 47 6.71

220 rows × 7 columns

Confirmed Cases by Country

In [15]:
dfww_confirmed = df5.sort_values(by='Confirmed', ascending=False).reset_index(drop=True)

dfww_confirmed.style.background_gradient(cmap='YlOrRd').format({'Confirmed': '{:.0f}', 'Deaths': '{:.0f}',
                                                              'Recovered': '{:.0f}', 'Active': '{:.0f}',
                                                               'Weeks Since 1st Case': '{:.2f}'})

#dfww_confirmed
Out[15]:
Country/Region Confirmed Deaths Recovered Active Days Since 1st Case Weeks Since 1st Case
0 US 939634 53786 101141 784707 95 13.57
1 Spain 223759 22902 95708 105149 85 12.14
2 Italy 195351 26384 63120 105847 86 12.29
3 Turkey 107773 2706 25582 79485 46 6.57
4 Iran 89328 5650 68193 15485 67 9.57
5 Mainland China 82827 4632 78225 -30 95 13.57
6 Russia 74588 681 6250 67657 86 12.29
7 Brazil 59324 4057 29160 26107 60 8.57
8 Canada 46357 2565 14 43778 91 13.00
9 Belgium 45325 6917 10417 27991 82 11.71
10 France 35456 1456 2949 31051 93 13.29
11 Switzerland 28894 1599 21300 5995 61 8.71
12 India 26283 825 5939 19519 87 12.43
13 Peru 25331 700 7797 16834 51 7.29
14 Portugal 23392 880 1277 21235 55 7.86
15 Ecuador 22719 576 1366 20777 56 8.00
16 Ireland 18561 1063 9233 8265 57 8.14
17 Sweden 18177 2192 1005 14980 86 12.29
18 Saudi Arabia 16299 136 2215 13948 55 7.86
19 Austria 15148 536 12103 2509 61 8.71
20 Mexico 13842 1305 7149 5388 58 8.29
21 Japan 13231 360 1656 11215 95 13.57
22 Chile 12858 181 6746 5931 54 7.71
23 Pakistan 12723 269 2866 9588 60 8.57
24 Singapore 12693 12 1002 11679 94 13.43
25 Poland 11273 524 2126 8623 53 7.57
26 South Korea 10728 242 8717 1769 95 13.57
27 Romania 10635 601 2890 7144 60 8.57
28 United Arab Emirates 9813 71 1887 7855 88 12.57
29 Belarus 9590 67 1573 7950 58 8.29
30 Qatar 9358 10 929 8419 57 8.14
31 Indonesia 8607 720 1042 6845 55 7.86
32 Ukraine 8125 201 782 7142 54 7.71
33 Norway 7499 201 32 7266 60 8.57
34 Czech Republic 7352 218 2453 4681 56 8.00
35 Philippines 7294 494 792 6008 87 12.43
36 Australia 6694 80 5271 1343 92 13.14
37 UK 6668 303 857 5508 86 12.29
38 Serbia 6630 125 870 5635 51 7.29
39 Dominican Republic 5926 273 822 4831 56 8.00
40 Malaysia 5742 98 3762 1882 92 13.14
41 Panama 5538 159 338 5041 47 6.71
42 Colombia 5142 233 1067 3842 51 7.29
43 Bangladesh 4998 140 113 4745 49 7.00
44 Finland 4475 186 2500 1789 88 12.57
45 South Africa 4361 86 1473 2802 52 7.43
46 Egypt 4319 307 1114 2898 72 10.29
47 Morocco 3897 159 537 3201 55 7.86
48 Netherlands 3825 151 104 3570 59 8.43
49 Argentina 3780 185 1030 2565 54 7.71
50 Luxembourg 3711 85 3088 538 57 8.14
51 Moldova 3304 94 825 2385 49 7.00
52 Algeria 3256 419 1479 1358 61 8.71
53 Thailand 2907 51 2547 309 95 13.57
54 Kuwait 2892 19 656 2217 62 8.86
55 Kazakhstan 2601 25 646 1930 44 6.29
56 Bahrain 2588 8 1160 1420 62 8.86
57 Greece 2506 130 577 1799 60 8.57
58 Hungary 2443 262 458 1723 53 7.57
59 Croatia 2016 54 1034 928 61 8.71
60 Oman 1905 10 329 1566 62 8.86
61 Uzbekistan 1862 8 707 1147 42 6.00
62 Iceland 1790 10 1570 210 58 8.29
63 Iraq 1763 86 1224 453 62 8.86
64 Armenia 1677 28 803 846 56 8.00
65 Estonia 1635 46 228 1361 59 8.43
66 Azerbaijan 1617 21 1080 516 56 8.00
67 Denmark 1524 13 190 1321 59 8.43
68 Cameroon 1518 53 697 768 51 7.29
69 Bosnia and Herzegovina 1486 57 592 837 52 7.43
70 New Zealand 1470 18 1142 310 58 8.29
71 Afghanistan 1463 47 188 1228 62 8.86
72 Lithuania 1426 41 460 925 58 8.29
73 Slovenia 1388 81 219 1088 52 7.43
74 Slovakia 1373 17 386 970 51 7.29
75 North Macedonia 1367 59 374 934 60 8.57
76 Cuba 1337 51 437 849 45 6.43
77 Ghana 1279 10 134 1135 43 6.14
78 Bulgaria 1247 55 197 995 49 7.00
79 Nigeria 1182 35 222 925 58 8.29
80 Ivory Coast 1077 14 419 644 90 12.86
81 Hong Kong 1037 4 753 280 94 13.43
82 Djibouti 1008 2 373 633 39 5.57
83 Guinea 996 7 208 781 44 6.29
84 Tunisia 939 38 207 694 53 7.57
85 Bolivia 866 46 54 766 46 6.57
86 Cyprus 810 14 148 648 48 6.86
87 Latvia 804 12 267 525 55 7.86
88 Andorra 738 40 344 354 55 7.86
89 Albania 712 27 403 282 48 6.86
90 Diamond Princess 712 13 645 54 32 4.57
91 Costa Rica 693 6 242 445 51 7.29
92 Niger 684 27 325 332 37 5.29
93 Kyrgyzstan 665 8 345 312 39 5.57
94 Burkina Faso 629 41 442 146 47 6.71
95 Honduras 627 59 65 503 46 6.57
96 Senegal 614 7 276 331 55 7.86
97 Uruguay 596 14 370 212 43 6.14
98 San Marino 513 40 64 409 59 8.43
99 Kosovo 510 12 93 405 42 6.00
100 West Bank and Gaza 484 4 92 388 31 4.43
101 Guatemala 473 13 45 415 43 6.14
102 Sri Lanka 460 7 118 335 90 12.86
103 Georgia 456 5 139 312 60 8.57
104 Malta 448 4 249 195 50 7.14
105 Jordan 444 7 332 105 54 7.71
106 Congo (Kinshasa) 416 28 49 339 46 6.57
107 Somalia 390 18 8 364 41 5.86
108 Mali 370 21 91 258 32 4.57
109 Kenya 343 14 98 231 44 6.29
110 Mauritius 331 9 295 27 39 5.57
111 Venezuela 323 10 132 181 43 6.14
112 Montenegro 320 6 153 161 40 5.71
113 Jamaica 305 7 28 270 46 6.57
114 Tanzania 299 10 48 241 41 5.86
115 El Salvador 274 8 75 191 38 5.43
116 Vietnam 270 0 225 45 94 13.43
117 Equatorial Guinea 258 1 7 250 42 6.00
118 Paraguay 228 9 85 134 49 7.00
119 Sudan 213 17 19 177 44 6.29
120 Congo (Brazzaville) 200 6 19 175 42 6.00
121 Rwanda 183 0 88 95 43 6.14
122 Maldives 177 0 17 160 49 7.00
123 Gabon 176 3 30 143 43 6.14
124 Burma 146 5 10 131 30 4.29
125 Brunei 138 1 121 16 48 6.86
126 Madagascar 123 0 62 61 37 5.29
127 Cambodia 122 0 117 5 90 12.86
128 Ethiopia 122 3 29 90 44 6.29
129 Liberia 120 11 25 84 41 5.86
130 Trinidad and Tobago 115 8 53 54 43 6.14
131 Togo 96 6 62 28 51 7.29
132 Monaco 94 4 42 48 57 8.14
133 Cabo Verde 90 1 1 88 37 5.29
134 Zambia 84 3 37 44 39 5.57
135 Sierra Leone 82 2 10 70 26 3.71
136 Liechtenstein 81 1 55 25 53 7.57
137 Barbados 79 6 31 42 40 5.71
138 Bahamas 78 11 15 52 35 5.00
139 Uganda 75 0 46 29 36 5.14
140 Guyana 73 7 12 54 45 6.43
141 Haiti 72 6 6 60 37 5.29
142 Mozambique 70 0 12 58 35 5.00
143 Others 61 0 0 61 79 11.29
144 Libya 61 2 18 41 33 4.71
145 Eswatini 56 1 10 45 43 6.14
146 Benin 54 1 27 26 41 5.86
147 Guadeloupe 53 0 0 53 44 6.29
148 Guinea-Bissau 52 0 3 49 32 4.57
149 Nepal 49 0 12 37 92 13.14
150 Taiwan 47 1 17 29 95 13.57
151 Chad 46 0 15 31 38 5.43
152 Macau 45 0 28 17 95 13.57
153 Reunion 45 0 0 45 46 6.57
154 Syria 42 3 11 28 35 5.00
155 Eritrea 39 0 13 26 36 5.14
156 Mongolia 37 0 9 28 47 6.71
157 Malawi 33 3 4 26 24 3.43
158 Martinique 32 1 0 31 50 7.14
159 Zimbabwe 31 4 2 25 37 5.29
160 occupied Palestinian territory 25 0 0 25 47 6.71
161 Angola 25 2 6 17 37 5.29
162 Antigua and Barbuda 24 3 11 10 44 6.29
163 Timor-Leste 24 0 2 22 35 5.00
164 Botswana 22 1 0 21 27 3.86
165 Palestine 22 0 0 22 52 7.43
166 Republic of Ireland 21 0 0 21 49 7.00
167 Laos 19 0 7 12 33 4.71
168 French Guiana 18 0 6 12 50 7.14
169 Grenada 18 0 7 11 35 5.00
170 Fiji 18 0 10 8 38 5.43
171 Belize 18 2 5 11 34 4.86
172 Namibia 16 0 7 9 43 6.14
173 Dominica 16 0 13 3 35 5.00
174 Central African Republic 16 0 10 6 42 6.00
175 Saint Lucia 15 0 15 0 43 6.14
176 Saint Kitts and Nevis 15 0 2 13 32 4.57
177 Saint Vincent and the Grenadines 14 0 5 9 43 6.14
178 Nicaragua 12 3 7 2 38 5.43
179 Burundi 11 1 4 6 26 3.71
180 Seychelles 11 0 6 5 43 6.14
181 Gambia 10 1 8 1 35 5.00
182 Suriname 10 1 7 2 43 6.14
183 Holy See 9 0 2 7 47 6.71
184 MS Zaandam 9 2 0 7 29 4.14
185 Israel 8 0 0 8 65 9.29
186 Papua New Guinea 8 0 0 8 37 5.29
187 Bhutan 7 0 3 4 51 7.29
188 Mauritania 7 1 6 0 43 6.14
189 Mayotte 7 0 0 7 41 5.86
190 Western Sahara 6 0 5 1 21 3.00
191 South Sudan 5 0 0 5 21 3.00
192 Germany 5 0 0 5 89 12.71
193 Bahamas, The 4 0 0 4 38 5.43
194 Sao Tome and Principe 4 0 0 4 20 2.86
195 Aruba 4 0 0 4 44 6.29
196 Puerto Rico 3 0 0 3 41 5.86
197 Guam 3 0 0 3 41 5.86
198 Saint Barthelemy 3 0 0 3 53 7.57
199 St. Martin 2 0 0 2 48 6.86
200 Faroe Islands 2 0 0 2 53 7.57
201 Lebanon 2 0 0 2 65 9.29
202 ('St. Martin',) 2 0 0 2 47 6.71
203 Jersey 2 0 0 2 43 6.14
204 Republic of the Congo 1 0 0 1 41 5.86
205 North Ireland 1 0 0 1 58 8.29
206 Yemen 1 0 1 0 16 2.29
207 Vatican City 1 0 0 1 51 7.29
208 Guernsey 1 0 0 1 43 6.14
209 The Bahamas 1 0 0 1 41 5.86
210 The Gambia 1 0 0 1 40 5.71
211 Cape Verde 1 0 0 1 36 5.14
212 Cayman Islands 1 0 0 1 44 6.29
213 Greenland 1 0 0 1 41 5.86
214 Channel Islands 1 0 0 1 47 6.71
215 Curacao 1 0 0 1 43 6.14
216 Gibraltar 1 0 1 0 53 7.57
217 East Timor 1 0 0 1 36 5.14
218 Gambia, The 1 0 0 1 39 5.57
219 Azerbaijan 1 0 0 1 58 8.29

Confirmed Deaths by Country

In [16]:
dfww_deaths = df5.sort_values(by='Deaths', ascending=False).reset_index(drop=True)

dfww_deaths.style.background_gradient(cmap='Reds').format({'Confirmed': '{:.0f}', 'Deaths': '{:.0f}',
                                                              'Recovered': '{:.0f}', 'Active': '{:.0f}',
                                                               'Weeks Since 1st Case': '{:.2f}'})
Out[16]:
Country/Region Confirmed Deaths Recovered Active Days Since 1st Case Weeks Since 1st Case
0 US 939634 53786 101141 784707 95 13.57
1 Italy 195351 26384 63120 105847 86 12.29
2 Spain 223759 22902 95708 105149 85 12.14
3 Belgium 45325 6917 10417 27991 82 11.71
4 Iran 89328 5650 68193 15485 67 9.57
5 Mainland China 82827 4632 78225 -30 95 13.57
6 Brazil 59324 4057 29160 26107 60 8.57
7 Turkey 107773 2706 25582 79485 46 6.57
8 Canada 46357 2565 14 43778 91 13.00
9 Sweden 18177 2192 1005 14980 86 12.29
10 Switzerland 28894 1599 21300 5995 61 8.71
11 France 35456 1456 2949 31051 93 13.29
12 Mexico 13842 1305 7149 5388 58 8.29
13 Ireland 18561 1063 9233 8265 57 8.14
14 Portugal 23392 880 1277 21235 55 7.86
15 India 26283 825 5939 19519 87 12.43
16 Indonesia 8607 720 1042 6845 55 7.86
17 Peru 25331 700 7797 16834 51 7.29
18 Russia 74588 681 6250 67657 86 12.29
19 Romania 10635 601 2890 7144 60 8.57
20 Ecuador 22719 576 1366 20777 56 8.00
21 Austria 15148 536 12103 2509 61 8.71
22 Poland 11273 524 2126 8623 53 7.57
23 Philippines 7294 494 792 6008 87 12.43
24 Algeria 3256 419 1479 1358 61 8.71
25 Japan 13231 360 1656 11215 95 13.57
26 Egypt 4319 307 1114 2898 72 10.29
27 UK 6668 303 857 5508 86 12.29
28 Dominican Republic 5926 273 822 4831 56 8.00
29 Pakistan 12723 269 2866 9588 60 8.57
30 Hungary 2443 262 458 1723 53 7.57
31 South Korea 10728 242 8717 1769 95 13.57
32 Colombia 5142 233 1067 3842 51 7.29
33 Czech Republic 7352 218 2453 4681 56 8.00
34 Ukraine 8125 201 782 7142 54 7.71
35 Norway 7499 201 32 7266 60 8.57
36 Finland 4475 186 2500 1789 88 12.57
37 Argentina 3780 185 1030 2565 54 7.71
38 Chile 12858 181 6746 5931 54 7.71
39 Morocco 3897 159 537 3201 55 7.86
40 Panama 5538 159 338 5041 47 6.71
41 Netherlands 3825 151 104 3570 59 8.43
42 Bangladesh 4998 140 113 4745 49 7.00
43 Saudi Arabia 16299 136 2215 13948 55 7.86
44 Greece 2506 130 577 1799 60 8.57
45 Serbia 6630 125 870 5635 51 7.29
46 Malaysia 5742 98 3762 1882 92 13.14
47 Moldova 3304 94 825 2385 49 7.00
48 South Africa 4361 86 1473 2802 52 7.43
49 Iraq 1763 86 1224 453 62 8.86
50 Luxembourg 3711 85 3088 538 57 8.14
51 Slovenia 1388 81 219 1088 52 7.43
52 Australia 6694 80 5271 1343 92 13.14
53 United Arab Emirates 9813 71 1887 7855 88 12.57
54 Belarus 9590 67 1573 7950 58 8.29
55 North Macedonia 1367 59 374 934 60 8.57
56 Honduras 627 59 65 503 46 6.57
57 Bosnia and Herzegovina 1486 57 592 837 52 7.43
58 Bulgaria 1247 55 197 995 49 7.00
59 Croatia 2016 54 1034 928 61 8.71
60 Cameroon 1518 53 697 768 51 7.29
61 Thailand 2907 51 2547 309 95 13.57
62 Cuba 1337 51 437 849 45 6.43
63 Afghanistan 1463 47 188 1228 62 8.86
64 Bolivia 866 46 54 766 46 6.57
65 Estonia 1635 46 228 1361 59 8.43
66 Lithuania 1426 41 460 925 58 8.29
67 Burkina Faso 629 41 442 146 47 6.71
68 Andorra 738 40 344 354 55 7.86
69 San Marino 513 40 64 409 59 8.43
70 Tunisia 939 38 207 694 53 7.57
71 Nigeria 1182 35 222 925 58 8.29
72 Armenia 1677 28 803 846 56 8.00
73 Congo (Kinshasa) 416 28 49 339 46 6.57
74 Albania 712 27 403 282 48 6.86
75 Niger 684 27 325 332 37 5.29
76 Kazakhstan 2601 25 646 1930 44 6.29
77 Mali 370 21 91 258 32 4.57
78 Azerbaijan 1617 21 1080 516 56 8.00
79 Kuwait 2892 19 656 2217 62 8.86
80 New Zealand 1470 18 1142 310 58 8.29
81 Somalia 390 18 8 364 41 5.86
82 Slovakia 1373 17 386 970 51 7.29
83 Sudan 213 17 19 177 44 6.29
84 Cyprus 810 14 148 648 48 6.86
85 Uruguay 596 14 370 212 43 6.14
86 Kenya 343 14 98 231 44 6.29
87 Ivory Coast 1077 14 419 644 90 12.86
88 Denmark 1524 13 190 1321 59 8.43
89 Guatemala 473 13 45 415 43 6.14
90 Diamond Princess 712 13 645 54 32 4.57
91 Kosovo 510 12 93 405 42 6.00
92 Latvia 804 12 267 525 55 7.86
93 Singapore 12693 12 1002 11679 94 13.43
94 Liberia 120 11 25 84 41 5.86
95 Bahamas 78 11 15 52 35 5.00
96 Iceland 1790 10 1570 210 58 8.29
97 Ghana 1279 10 134 1135 43 6.14
98 Venezuela 323 10 132 181 43 6.14
99 Oman 1905 10 329 1566 62 8.86
100 Qatar 9358 10 929 8419 57 8.14
101 Tanzania 299 10 48 241 41 5.86
102 Mauritius 331 9 295 27 39 5.57
103 Paraguay 228 9 85 134 49 7.00
104 Kyrgyzstan 665 8 345 312 39 5.57
105 Uzbekistan 1862 8 707 1147 42 6.00
106 Bahrain 2588 8 1160 1420 62 8.86
107 El Salvador 274 8 75 191 38 5.43
108 Trinidad and Tobago 115 8 53 54 43 6.14
109 Senegal 614 7 276 331 55 7.86
110 Guyana 73 7 12 54 45 6.43
111 Guinea 996 7 208 781 44 6.29
112 Jamaica 305 7 28 270 46 6.57
113 Sri Lanka 460 7 118 335 90 12.86
114 Jordan 444 7 332 105 54 7.71
115 Haiti 72 6 6 60 37 5.29
116 Barbados 79 6 31 42 40 5.71
117 Togo 96 6 62 28 51 7.29
118 Congo (Brazzaville) 200 6 19 175 42 6.00
119 Costa Rica 693 6 242 445 51 7.29
120 Montenegro 320 6 153 161 40 5.71
121 Burma 146 5 10 131 30 4.29
122 Georgia 456 5 139 312 60 8.57
123 Zimbabwe 31 4 2 25 37 5.29
124 Malta 448 4 249 195 50 7.14
125 Hong Kong 1037 4 753 280 94 13.43
126 Monaco 94 4 42 48 57 8.14
127 West Bank and Gaza 484 4 92 388 31 4.43
128 Malawi 33 3 4 26 24 3.43
129 Zambia 84 3 37 44 39 5.57
130 Syria 42 3 11 28 35 5.00
131 Gabon 176 3 30 143 43 6.14
132 Ethiopia 122 3 29 90 44 6.29
133 Nicaragua 12 3 7 2 38 5.43
134 Antigua and Barbuda 24 3 11 10 44 6.29
135 Djibouti 1008 2 373 633 39 5.57
136 Belize 18 2 5 11 34 4.86
137 Sierra Leone 82 2 10 70 26 3.71
138 Libya 61 2 18 41 33 4.71
139 Angola 25 2 6 17 37 5.29
140 MS Zaandam 9 2 0 7 29 4.14
141 Liechtenstein 81 1 55 25 53 7.57
142 Equatorial Guinea 258 1 7 250 42 6.00
143 Taiwan 47 1 17 29 95 13.57
144 Suriname 10 1 7 2 43 6.14
145 Eswatini 56 1 10 45 43 6.14
146 Gambia 10 1 8 1 35 5.00
147 Cabo Verde 90 1 1 88 37 5.29
148 Benin 54 1 27 26 41 5.86
149 Burundi 11 1 4 6 26 3.71
150 Mauritania 7 1 6 0 43 6.14
151 Martinique 32 1 0 31 50 7.14
152 Brunei 138 1 121 16 48 6.86
153 Botswana 22 1 0 21 27 3.86
154 Sao Tome and Principe 4 0 0 4 20 2.86
155 Saint Vincent and the Grenadines 14 0 5 9 43 6.14
156 Uganda 75 0 46 29 36 5.14
157 Yemen 1 0 1 0 16 2.29
158 Seychelles 11 0 6 5 43 6.14
159 Western Sahara 6 0 5 1 21 3.00
160 Vietnam 270 0 225 45 94 13.43
161 Vatican City 1 0 0 1 51 7.29
162 South Sudan 5 0 0 5 21 3.00
163 Timor-Leste 24 0 2 22 35 5.00
164 The Gambia 1 0 0 1 40 5.71
165 The Bahamas 1 0 0 1 41 5.86
166 Saint Kitts and Nevis 15 0 2 13 32 4.57
167 St. Martin 2 0 0 2 48 6.86
168 Saint Lucia 15 0 15 0 43 6.14
169 Azerbaijan 1 0 0 1 58 8.29
170 Saint Barthelemy 3 0 0 3 53 7.57
171 East Timor 1 0 0 1 36 5.14
172 Guadeloupe 53 0 0 53 44 6.29
173 Grenada 18 0 7 11 35 5.00
174 Greenland 1 0 0 1 41 5.86
175 Gibraltar 1 0 1 0 53 7.57
176 Germany 5 0 0 5 89 12.71
177 Gambia, The 1 0 0 1 39 5.57
178 French Guiana 18 0 6 12 50 7.14
179 Fiji 18 0 10 8 38 5.43
180 Faroe Islands 2 0 0 2 53 7.57
181 Eritrea 39 0 13 26 36 5.14
182 Dominica 16 0 13 3 35 5.00
183 Rwanda 183 0 88 95 43 6.14
184 Curacao 1 0 0 1 43 6.14
185 Channel Islands 1 0 0 1 47 6.71
186 Chad 46 0 15 31 38 5.43
187 Central African Republic 16 0 10 6 42 6.00
188 Cayman Islands 1 0 0 1 44 6.29
189 Cape Verde 1 0 0 1 36 5.14
190 Cambodia 122 0 117 5 90 12.86
191 Bhutan 7 0 3 4 51 7.29
192 Bahamas, The 4 0 0 4 38 5.43
193 Aruba 4 0 0 4 44 6.29
194 Guam 3 0 0 3 41 5.86
195 Guernsey 1 0 0 1 43 6.14
196 Guinea-Bissau 52 0 3 49 32 4.57
197 Holy See 9 0 2 7 47 6.71
198 Reunion 45 0 0 45 46 6.57
199 Republic of the Congo 1 0 0 1 41 5.86
200 Republic of Ireland 21 0 0 21 49 7.00
201 Puerto Rico 3 0 0 3 41 5.86
202 Papua New Guinea 8 0 0 8 37 5.29
203 Palestine 22 0 0 22 52 7.43
204 Others 61 0 0 61 79 11.29
205 North Ireland 1 0 0 1 58 8.29
206 Nepal 49 0 12 37 92 13.14
207 Namibia 16 0 7 9 43 6.14
208 Mozambique 70 0 12 58 35 5.00
209 Mongolia 37 0 9 28 47 6.71
210 Mayotte 7 0 0 7 41 5.86
211 Maldives 177 0 17 160 49 7.00
212 Madagascar 123 0 62 61 37 5.29
213 Macau 45 0 28 17 95 13.57
214 Lebanon 2 0 0 2 65 9.29
215 Laos 19 0 7 12 33 4.71
216 ('St. Martin',) 2 0 0 2 47 6.71
217 Jersey 2 0 0 2 43 6.14
218 Israel 8 0 0 8 65 9.29
219 occupied Palestinian territory 25 0 0 25 47 6.71

Recovered Cases by Country

In [17]:
dfww_recovered = df5.sort_values(by='Recovered', ascending=False).reset_index(drop=True)

dfww_recovered.style.background_gradient(cmap='Greens').format({'Confirmed': '{:.0f}', 'Deaths': '{:.0f}',
                                                              'Recovered': '{:.0f}', 'Active': '{:.0f}',
                                                               'Weeks Since 1st Case': '{:.2f}'})
Out[17]:
Country/Region Confirmed Deaths Recovered Active Days Since 1st Case Weeks Since 1st Case
0 US 939634 53786 101141 784707 95 13.57
1 Spain 223759 22902 95708 105149 85 12.14
2 Mainland China 82827 4632 78225 -30 95 13.57
3 Iran 89328 5650 68193 15485 67 9.57
4 Italy 195351 26384 63120 105847 86 12.29
5 Brazil 59324 4057 29160 26107 60 8.57
6 Turkey 107773 2706 25582 79485 46 6.57
7 Switzerland 28894 1599 21300 5995 61 8.71
8 Austria 15148 536 12103 2509 61 8.71
9 Belgium 45325 6917 10417 27991 82 11.71
10 Ireland 18561 1063 9233 8265 57 8.14
11 South Korea 10728 242 8717 1769 95 13.57
12 Peru 25331 700 7797 16834 51 7.29
13 Mexico 13842 1305 7149 5388 58 8.29
14 Chile 12858 181 6746 5931 54 7.71
15 Russia 74588 681 6250 67657 86 12.29
16 India 26283 825 5939 19519 87 12.43
17 Australia 6694 80 5271 1343 92 13.14
18 Malaysia 5742 98 3762 1882 92 13.14
19 Luxembourg 3711 85 3088 538 57 8.14
20 France 35456 1456 2949 31051 93 13.29
21 Romania 10635 601 2890 7144 60 8.57
22 Pakistan 12723 269 2866 9588 60 8.57
23 Thailand 2907 51 2547 309 95 13.57
24 Finland 4475 186 2500 1789 88 12.57
25 Czech Republic 7352 218 2453 4681 56 8.00
26 Saudi Arabia 16299 136 2215 13948 55 7.86
27 Poland 11273 524 2126 8623 53 7.57
28 United Arab Emirates 9813 71 1887 7855 88 12.57
29 Japan 13231 360 1656 11215 95 13.57
30 Belarus 9590 67 1573 7950 58 8.29
31 Iceland 1790 10 1570 210 58 8.29
32 Algeria 3256 419 1479 1358 61 8.71
33 South Africa 4361 86 1473 2802 52 7.43
34 Ecuador 22719 576 1366 20777 56 8.00
35 Portugal 23392 880 1277 21235 55 7.86
36 Iraq 1763 86 1224 453 62 8.86
37 Bahrain 2588 8 1160 1420 62 8.86
38 New Zealand 1470 18 1142 310 58 8.29
39 Egypt 4319 307 1114 2898 72 10.29
40 Azerbaijan 1617 21 1080 516 56 8.00
41 Colombia 5142 233 1067 3842 51 7.29
42 Indonesia 8607 720 1042 6845 55 7.86
43 Croatia 2016 54 1034 928 61 8.71
44 Argentina 3780 185 1030 2565 54 7.71
45 Sweden 18177 2192 1005 14980 86 12.29
46 Singapore 12693 12 1002 11679 94 13.43
47 Qatar 9358 10 929 8419 57 8.14
48 Serbia 6630 125 870 5635 51 7.29
49 UK 6668 303 857 5508 86 12.29
50 Moldova 3304 94 825 2385 49 7.00
51 Dominican Republic 5926 273 822 4831 56 8.00
52 Armenia 1677 28 803 846 56 8.00
53 Philippines 7294 494 792 6008 87 12.43
54 Ukraine 8125 201 782 7142 54 7.71
55 Hong Kong 1037 4 753 280 94 13.43
56 Uzbekistan 1862 8 707 1147 42 6.00
57 Cameroon 1518 53 697 768 51 7.29
58 Kuwait 2892 19 656 2217 62 8.86
59 Kazakhstan 2601 25 646 1930 44 6.29
60 Diamond Princess 712 13 645 54 32 4.57
61 Bosnia and Herzegovina 1486 57 592 837 52 7.43
62 Greece 2506 130 577 1799 60 8.57
63 Morocco 3897 159 537 3201 55 7.86
64 Lithuania 1426 41 460 925 58 8.29
65 Hungary 2443 262 458 1723 53 7.57
66 Burkina Faso 629 41 442 146 47 6.71
67 Cuba 1337 51 437 849 45 6.43
68 Ivory Coast 1077 14 419 644 90 12.86
69 Albania 712 27 403 282 48 6.86
70 Slovakia 1373 17 386 970 51 7.29
71 North Macedonia 1367 59 374 934 60 8.57
72 Djibouti 1008 2 373 633 39 5.57
73 Uruguay 596 14 370 212 43 6.14
74 Kyrgyzstan 665 8 345 312 39 5.57
75 Andorra 738 40 344 354 55 7.86
76 Panama 5538 159 338 5041 47 6.71
77 Jordan 444 7 332 105 54 7.71
78 Oman 1905 10 329 1566 62 8.86
79 Niger 684 27 325 332 37 5.29
80 Mauritius 331 9 295 27 39 5.57
81 Senegal 614 7 276 331 55 7.86
82 Latvia 804 12 267 525 55 7.86
83 Malta 448 4 249 195 50 7.14
84 Costa Rica 693 6 242 445 51 7.29
85 Estonia 1635 46 228 1361 59 8.43
86 Vietnam 270 0 225 45 94 13.43
87 Nigeria 1182 35 222 925 58 8.29
88 Slovenia 1388 81 219 1088 52 7.43
89 Guinea 996 7 208 781 44 6.29
90 Tunisia 939 38 207 694 53 7.57
91 Bulgaria 1247 55 197 995 49 7.00
92 Denmark 1524 13 190 1321 59 8.43
93 Afghanistan 1463 47 188 1228 62 8.86
94 Montenegro 320 6 153 161 40 5.71
95 Cyprus 810 14 148 648 48 6.86
96 Georgia 456 5 139 312 60 8.57
97 Ghana 1279 10 134 1135 43 6.14
98 Venezuela 323 10 132 181 43 6.14
99 Brunei 138 1 121 16 48 6.86
100 Sri Lanka 460 7 118 335 90 12.86
101 Cambodia 122 0 117 5 90 12.86
102 Bangladesh 4998 140 113 4745 49 7.00
103 Netherlands 3825 151 104 3570 59 8.43
104 Kenya 343 14 98 231 44 6.29
105 Kosovo 510 12 93 405 42 6.00
106 West Bank and Gaza 484 4 92 388 31 4.43
107 Mali 370 21 91 258 32 4.57
108 Rwanda 183 0 88 95 43 6.14
109 Paraguay 228 9 85 134 49 7.00
110 El Salvador 274 8 75 191 38 5.43
111 Honduras 627 59 65 503 46 6.57
112 San Marino 513 40 64 409 59 8.43
113 Madagascar 123 0 62 61 37 5.29
114 Togo 96 6 62 28 51 7.29
115 Liechtenstein 81 1 55 25 53 7.57
116 Bolivia 866 46 54 766 46 6.57
117 Trinidad and Tobago 115 8 53 54 43 6.14
118 Congo (Kinshasa) 416 28 49 339 46 6.57
119 Tanzania 299 10 48 241 41 5.86
120 Uganda 75 0 46 29 36 5.14
121 Guatemala 473 13 45 415 43 6.14
122 Monaco 94 4 42 48 57 8.14
123 Zambia 84 3 37 44 39 5.57
124 Norway 7499 201 32 7266 60 8.57
125 Barbados 79 6 31 42 40 5.71
126 Gabon 176 3 30 143 43 6.14
127 Ethiopia 122 3 29 90 44 6.29
128 Jamaica 305 7 28 270 46 6.57
129 Macau 45 0 28 17 95 13.57
130 Benin 54 1 27 26 41 5.86
131 Liberia 120 11 25 84 41 5.86
132 Sudan 213 17 19 177 44 6.29
133 Congo (Brazzaville) 200 6 19 175 42 6.00
134 Libya 61 2 18 41 33 4.71
135 Taiwan 47 1 17 29 95 13.57
136 Maldives 177 0 17 160 49 7.00
137 Saint Lucia 15 0 15 0 43 6.14
138 Bahamas 78 11 15 52 35 5.00
139 Chad 46 0 15 31 38 5.43
140 Canada 46357 2565 14 43778 91 13.00
141 Dominica 16 0 13 3 35 5.00
142 Eritrea 39 0 13 26 36 5.14
143 Nepal 49 0 12 37 92 13.14
144 Mozambique 70 0 12 58 35 5.00
145 Guyana 73 7 12 54 45 6.43
146 Syria 42 3 11 28 35 5.00
147 Antigua and Barbuda 24 3 11 10 44 6.29
148 Eswatini 56 1 10 45 43 6.14
149 Fiji 18 0 10 8 38 5.43
150 Central African Republic 16 0 10 6 42 6.00
151 Sierra Leone 82 2 10 70 26 3.71
152 Burma 146 5 10 131 30 4.29
153 Mongolia 37 0 9 28 47 6.71
154 Somalia 390 18 8 364 41 5.86
155 Gambia 10 1 8 1 35 5.00
156 Suriname 10 1 7 2 43 6.14
157 Grenada 18 0 7 11 35 5.00
158 Namibia 16 0 7 9 43 6.14
159 Laos 19 0 7 12 33 4.71
160 Equatorial Guinea 258 1 7 250 42 6.00
161 Nicaragua 12 3 7 2 38 5.43
162 French Guiana 18 0 6 12 50 7.14
163 Haiti 72 6 6 60 37 5.29
164 Angola 25 2 6 17 37 5.29
165 Mauritania 7 1 6 0 43 6.14
166 Seychelles 11 0 6 5 43 6.14
167 Saint Vincent and the Grenadines 14 0 5 9 43 6.14
168 Western Sahara 6 0 5 1 21 3.00
169 Belize 18 2 5 11 34 4.86
170 Malawi 33 3 4 26 24 3.43
171 Burundi 11 1 4 6 26 3.71
172 Bhutan 7 0 3 4 51 7.29
173 Guinea-Bissau 52 0 3 49 32 4.57
174 Saint Kitts and Nevis 15 0 2 13 32 4.57
175 Zimbabwe 31 4 2 25 37 5.29
176 Holy See 9 0 2 7 47 6.71
177 Timor-Leste 24 0 2 22 35 5.00
178 Gibraltar 1 0 1 0 53 7.57
179 Yemen 1 0 1 0 16 2.29
180 Cabo Verde 90 1 1 88 37 5.29
181 Vatican City 1 0 0 1 51 7.29
182 St. Martin 2 0 0 2 48 6.86
183 The Bahamas 1 0 0 1 41 5.86
184 The Gambia 1 0 0 1 40 5.71
185 Azerbaijan 1 0 0 1 58 8.29
186 South Sudan 5 0 0 5 21 3.00
187 East Timor 1 0 0 1 36 5.14
188 Guam 3 0 0 3 41 5.86
189 Guadeloupe 53 0 0 53 44 6.29
190 Greenland 1 0 0 1 41 5.86
191 Germany 5 0 0 5 89 12.71
192 Gambia, The 1 0 0 1 39 5.57
193 Faroe Islands 2 0 0 2 53 7.57
194 Curacao 1 0 0 1 43 6.14
195 Sao Tome and Principe 4 0 0 4 20 2.86
196 Channel Islands 1 0 0 1 47 6.71
197 Cayman Islands 1 0 0 1 44 6.29
198 Cape Verde 1 0 0 1 36 5.14
199 Botswana 22 1 0 21 27 3.86
200 Bahamas, The 4 0 0 4 38 5.43
201 Aruba 4 0 0 4 44 6.29
202 Guernsey 1 0 0 1 43 6.14
203 Israel 8 0 0 8 65 9.29
204 Jersey 2 0 0 2 43 6.14
205 ('St. Martin',) 2 0 0 2 47 6.71
206 Lebanon 2 0 0 2 65 9.29
207 MS Zaandam 9 2 0 7 29 4.14
208 Martinique 32 1 0 31 50 7.14
209 Mayotte 7 0 0 7 41 5.86
210 North Ireland 1 0 0 1 58 8.29
211 Others 61 0 0 61 79 11.29
212 Palestine 22 0 0 22 52 7.43
213 Papua New Guinea 8 0 0 8 37 5.29
214 Puerto Rico 3 0 0 3 41 5.86
215 Republic of Ireland 21 0 0 21 49 7.00
216 Republic of the Congo 1 0 0 1 41 5.86
217 Reunion 45 0 0 45 46 6.57
218 Saint Barthelemy 3 0 0 3 53 7.57
219 occupied Palestinian territory 25 0 0 25 47 6.71

Active Cases by Country

In [18]:
dfww_active = df5.sort_values(by='Active', ascending=False).reset_index(drop=True)

dfww_active.style.background_gradient(cmap='YlOrRd').format({'Confirmed': '{:.0f}', 'Deaths': '{:.0f}',
                                                              'Recovered': '{:.0f}', 'Active': '{:.0f}',
                                                               'Weeks Since 1st Case': '{:.2f}'})
Out[18]:
Country/Region Confirmed Deaths Recovered Active Days Since 1st Case Weeks Since 1st Case
0 US 939634 53786 101141 784707 95 13.57
1 Italy 195351 26384 63120 105847 86 12.29
2 Spain 223759 22902 95708 105149 85 12.14
3 Turkey 107773 2706 25582 79485 46 6.57
4 Russia 74588 681 6250 67657 86 12.29
5 Canada 46357 2565 14 43778 91 13.00
6 France 35456 1456 2949 31051 93 13.29
7 Belgium 45325 6917 10417 27991 82 11.71
8 Brazil 59324 4057 29160 26107 60 8.57
9 Portugal 23392 880 1277 21235 55 7.86
10 Ecuador 22719 576 1366 20777 56 8.00
11 India 26283 825 5939 19519 87 12.43
12 Peru 25331 700 7797 16834 51 7.29
13 Iran 89328 5650 68193 15485 67 9.57
14 Sweden 18177 2192 1005 14980 86 12.29
15 Saudi Arabia 16299 136 2215 13948 55 7.86
16 Singapore 12693 12 1002 11679 94 13.43
17 Japan 13231 360 1656 11215 95 13.57
18 Pakistan 12723 269 2866 9588 60 8.57
19 Poland 11273 524 2126 8623 53 7.57
20 Qatar 9358 10 929 8419 57 8.14
21 Ireland 18561 1063 9233 8265 57 8.14
22 Belarus 9590 67 1573 7950 58 8.29
23 United Arab Emirates 9813 71 1887 7855 88 12.57
24 Norway 7499 201 32 7266 60 8.57
25 Romania 10635 601 2890 7144 60 8.57
26 Ukraine 8125 201 782 7142 54 7.71
27 Indonesia 8607 720 1042 6845 55 7.86
28 Philippines 7294 494 792 6008 87 12.43
29 Switzerland 28894 1599 21300 5995 61 8.71
30 Chile 12858 181 6746 5931 54 7.71
31 Serbia 6630 125 870 5635 51 7.29
32 UK 6668 303 857 5508 86 12.29
33 Mexico 13842 1305 7149 5388 58 8.29
34 Panama 5538 159 338 5041 47 6.71
35 Dominican Republic 5926 273 822 4831 56 8.00
36 Bangladesh 4998 140 113 4745 49 7.00
37 Czech Republic 7352 218 2453 4681 56 8.00
38 Colombia 5142 233 1067 3842 51 7.29
39 Netherlands 3825 151 104 3570 59 8.43
40 Morocco 3897 159 537 3201 55 7.86
41 Egypt 4319 307 1114 2898 72 10.29
42 South Africa 4361 86 1473 2802 52 7.43
43 Argentina 3780 185 1030 2565 54 7.71
44 Austria 15148 536 12103 2509 61 8.71
45 Moldova 3304 94 825 2385 49 7.00
46 Kuwait 2892 19 656 2217 62 8.86
47 Kazakhstan 2601 25 646 1930 44 6.29
48 Malaysia 5742 98 3762 1882 92 13.14
49 Greece 2506 130 577 1799 60 8.57
50 Finland 4475 186 2500 1789 88 12.57
51 South Korea 10728 242 8717 1769 95 13.57
52 Hungary 2443 262 458 1723 53 7.57
53 Oman 1905 10 329 1566 62 8.86
54 Bahrain 2588 8 1160 1420 62 8.86
55 Estonia 1635 46 228 1361 59 8.43
56 Algeria 3256 419 1479 1358 61 8.71
57 Australia 6694 80 5271 1343 92 13.14
58 Denmark 1524 13 190 1321 59 8.43
59 Afghanistan 1463 47 188 1228 62 8.86
60 Uzbekistan 1862 8 707 1147 42 6.00
61 Ghana 1279 10 134 1135 43 6.14
62 Slovenia 1388 81 219 1088 52 7.43
63 Bulgaria 1247 55 197 995 49 7.00
64 Slovakia 1373 17 386 970 51 7.29
65 North Macedonia 1367 59 374 934 60 8.57
66 Croatia 2016 54 1034 928 61 8.71
67 Lithuania 1426 41 460 925 58 8.29
68 Nigeria 1182 35 222 925 58 8.29
69 Cuba 1337 51 437 849 45 6.43
70 Armenia 1677 28 803 846 56 8.00
71 Bosnia and Herzegovina 1486 57 592 837 52 7.43
72 Guinea 996 7 208 781 44 6.29
73 Cameroon 1518 53 697 768 51 7.29
74 Bolivia 866 46 54 766 46 6.57
75 Tunisia 939 38 207 694 53 7.57
76 Cyprus 810 14 148 648 48 6.86
77 Ivory Coast 1077 14 419 644 90 12.86
78 Djibouti 1008 2 373 633 39 5.57
79 Luxembourg 3711 85 3088 538 57 8.14
80 Latvia 804 12 267 525 55 7.86
81 Azerbaijan 1617 21 1080 516 56 8.00
82 Honduras 627 59 65 503 46 6.57
83 Iraq 1763 86 1224 453 62 8.86
84 Costa Rica 693 6 242 445 51 7.29
85 Guatemala 473 13 45 415 43 6.14
86 San Marino 513 40 64 409 59 8.43
87 Kosovo 510 12 93 405 42 6.00
88 West Bank and Gaza 484 4 92 388 31 4.43
89 Somalia 390 18 8 364 41 5.86
90 Andorra 738 40 344 354 55 7.86
91 Congo (Kinshasa) 416 28 49 339 46 6.57
92 Sri Lanka 460 7 118 335 90 12.86
93 Niger 684 27 325 332 37 5.29
94 Senegal 614 7 276 331 55 7.86
95 Kyrgyzstan 665 8 345 312 39 5.57
96 Georgia 456 5 139 312 60 8.57
97 New Zealand 1470 18 1142 310 58 8.29
98 Thailand 2907 51 2547 309 95 13.57
99 Albania 712 27 403 282 48 6.86
100 Hong Kong 1037 4 753 280 94 13.43
101 Jamaica 305 7 28 270 46 6.57
102 Mali 370 21 91 258 32 4.57
103 Equatorial Guinea 258 1 7 250 42 6.00
104 Tanzania 299 10 48 241 41 5.86
105 Kenya 343 14 98 231 44 6.29
106 Uruguay 596 14 370 212 43 6.14
107 Iceland 1790 10 1570 210 58 8.29
108 Malta 448 4 249 195 50 7.14
109 El Salvador 274 8 75 191 38 5.43
110 Venezuela 323 10 132 181 43 6.14
111 Sudan 213 17 19 177 44 6.29
112 Congo (Brazzaville) 200 6 19 175 42 6.00
113 Montenegro 320 6 153 161 40 5.71
114 Maldives 177 0 17 160 49 7.00
115 Burkina Faso 629 41 442 146 47 6.71
116 Gabon 176 3 30 143 43 6.14
117 Paraguay 228 9 85 134 49 7.00
118 Burma 146 5 10 131 30 4.29
119 Jordan 444 7 332 105 54 7.71
120 Rwanda 183 0 88 95 43 6.14
121 Ethiopia 122 3 29 90 44 6.29
122 Cabo Verde 90 1 1 88 37 5.29
123 Liberia 120 11 25 84 41 5.86
124 Sierra Leone 82 2 10 70 26 3.71
125 Madagascar 123 0 62 61 37 5.29
126 Others 61 0 0 61 79 11.29
127 Haiti 72 6 6 60 37 5.29
128 Mozambique 70 0 12 58 35 5.00
129 Trinidad and Tobago 115 8 53 54 43 6.14
130 Guyana 73 7 12 54 45 6.43
131 Diamond Princess 712 13 645 54 32 4.57
132 Guadeloupe 53 0 0 53 44 6.29
133 Bahamas 78 11 15 52 35 5.00
134 Guinea-Bissau 52 0 3 49 32 4.57
135 Monaco 94 4 42 48 57 8.14
136 Vietnam 270 0 225 45 94 13.43
137 Reunion 45 0 0 45 46 6.57
138 Eswatini 56 1 10 45 43 6.14
139 Zambia 84 3 37 44 39 5.57
140 Barbados 79 6 31 42 40 5.71
141 Libya 61 2 18 41 33 4.71
142 Nepal 49 0 12 37 92 13.14
143 Chad 46 0 15 31 38 5.43
144 Martinique 32 1 0 31 50 7.14
145 Taiwan 47 1 17 29 95 13.57
146 Uganda 75 0 46 29 36 5.14
147 Syria 42 3 11 28 35 5.00
148 Mongolia 37 0 9 28 47 6.71
149 Togo 96 6 62 28 51 7.29
150 Mauritius 331 9 295 27 39 5.57
151 Benin 54 1 27 26 41 5.86
152 Eritrea 39 0 13 26 36 5.14
153 Malawi 33 3 4 26 24 3.43
154 Zimbabwe 31 4 2 25 37 5.29
155 occupied Palestinian territory 25 0 0 25 47 6.71
156 Liechtenstein 81 1 55 25 53 7.57
157 Timor-Leste 24 0 2 22 35 5.00
158 Palestine 22 0 0 22 52 7.43
159 Republic of Ireland 21 0 0 21 49 7.00
160 Botswana 22 1 0 21 27 3.86
161 Angola 25 2 6 17 37 5.29
162 Macau 45 0 28 17 95 13.57
163 Brunei 138 1 121 16 48 6.86
164 Saint Kitts and Nevis 15 0 2 13 32 4.57
165 French Guiana 18 0 6 12 50 7.14
166 Laos 19 0 7 12 33 4.71
167 Belize 18 2 5 11 34 4.86
168 Grenada 18 0 7 11 35 5.00
169 Antigua and Barbuda 24 3 11 10 44 6.29
170 Namibia 16 0 7 9 43 6.14
171 Saint Vincent and the Grenadines 14 0 5 9 43 6.14
172 Israel 8 0 0 8 65 9.29
173 Papua New Guinea 8 0 0 8 37 5.29
174 Fiji 18 0 10 8 38 5.43
175 Holy See 9 0 2 7 47 6.71
176 MS Zaandam 9 2 0 7 29 4.14
177 Mayotte 7 0 0 7 41 5.86
178 Burundi 11 1 4 6 26 3.71
179 Central African Republic 16 0 10 6 42 6.00
180 South Sudan 5 0 0 5 21 3.00
181 Germany 5 0 0 5 89 12.71
182 Seychelles 11 0 6 5 43 6.14
183 Cambodia 122 0 117 5 90 12.86
184 Aruba 4 0 0 4 44 6.29
185 Sao Tome and Principe 4 0 0 4 20 2.86
186 Bahamas, The 4 0 0 4 38 5.43
187 Bhutan 7 0 3 4 51 7.29
188 Dominica 16 0 13 3 35 5.00
189 Saint Barthelemy 3 0 0 3 53 7.57
190 Puerto Rico 3 0 0 3 41 5.86
191 Guam 3 0 0 3 41 5.86
192 Faroe Islands 2 0 0 2 53 7.57
193 St. Martin 2 0 0 2 48 6.86
194 Jersey 2 0 0 2 43 6.14
195 Nicaragua 12 3 7 2 38 5.43
196 Suriname 10 1 7 2 43 6.14
197 Lebanon 2 0 0 2 65 9.29
198 ('St. Martin',) 2 0 0 2 47 6.71
199 Guernsey 1 0 0 1 43 6.14
200 North Ireland 1 0 0 1 58 8.29
201 Western Sahara 6 0 5 1 21 3.00
202 Republic of the Congo 1 0 0 1 41 5.86
203 Vatican City 1 0 0 1 51 7.29
204 Gambia, The 1 0 0 1 39 5.57
205 Gambia 10 1 8 1 35 5.00
206 Cape Verde 1 0 0 1 36 5.14
207 Cayman Islands 1 0 0 1 44 6.29
208 Greenland 1 0 0 1 41 5.86
209 Channel Islands 1 0 0 1 47 6.71
210 Curacao 1 0 0 1 43 6.14
211 The Gambia 1 0 0 1 40 5.71
212 The Bahamas 1 0 0 1 41 5.86
213 East Timor 1 0 0 1 36 5.14
214 Azerbaijan 1 0 0 1 58 8.29
215 Saint Lucia 15 0 15 0 43 6.14
216 Mauritania 7 1 6 0 43 6.14
217 Gibraltar 1 0 1 0 53 7.57
218 Yemen 1 0 1 0 16 2.29
219 Mainland China 82827 4632 78225 -30 95 13.57

Recovery Percent by Country (Recovered / Confirmed Cases)

In [19]:
dfww_recovered['Recovered Percent'] = round((dfww_recovered['Recovered'] / dfww_recovered['Confirmed']), 4)

dfww_recovered = dfww_recovered[['Country/Region', 
                                 'Confirmed', 'Deaths', 'Recovered', 
                                 'Active', 'Recovered Percent', 
                                 'Days Since 1st Case', 'Weeks Since 1st Case']]

dfww_recovered = dfww_recovered.loc[dfww_recovered['Confirmed'] > 2000, :]

dfww_recovered.sort_values(by='Recovered Percent', ascending=False)\
.reset_index(drop=True).style.background_gradient(cmap='Greens').format({'Confirmed': '{:.0f}', 'Deaths': '{:.0f}',
                                                              'Recovered': '{:.0f}', 'Active': '{:.0f}',
                                                               'Recovered Percent': '{:.2%}', 'Weeks Since 1st Case': '{:.2f}'})
Out[19]:
Country/Region Confirmed Deaths Recovered Active Recovered Percent Days Since 1st Case Weeks Since 1st Case
0 Mainland China 82827 4632 78225 -30 94.44% 95 13.57
1 Thailand 2907 51 2547 309 87.62% 95 13.57
2 Luxembourg 3711 85 3088 538 83.21% 57 8.14
3 South Korea 10728 242 8717 1769 81.25% 95 13.57
4 Austria 15148 536 12103 2509 79.90% 61 8.71
5 Australia 6694 80 5271 1343 78.74% 92 13.14
6 Iran 89328 5650 68193 15485 76.34% 67 9.57
7 Switzerland 28894 1599 21300 5995 73.72% 61 8.71
8 Malaysia 5742 98 3762 1882 65.52% 92 13.14
9 Finland 4475 186 2500 1789 55.87% 88 12.57
10 Chile 12858 181 6746 5931 52.47% 54 7.71
11 Mexico 13842 1305 7149 5388 51.65% 58 8.29
12 Croatia 2016 54 1034 928 51.29% 61 8.71
13 Ireland 18561 1063 9233 8265 49.74% 57 8.14
14 Brazil 59324 4057 29160 26107 49.15% 60 8.57
15 Algeria 3256 419 1479 1358 45.42% 61 8.71
16 Bahrain 2588 8 1160 1420 44.82% 62 8.86
17 Spain 223759 22902 95708 105149 42.77% 85 12.14
18 South Africa 4361 86 1473 2802 33.78% 52 7.43
19 Czech Republic 7352 218 2453 4681 33.37% 56 8.00
20 Italy 195351 26384 63120 105847 32.31% 86 12.29
21 Peru 25331 700 7797 16834 30.78% 51 7.29
22 Argentina 3780 185 1030 2565 27.25% 54 7.71
23 Romania 10635 601 2890 7144 27.17% 60 8.57
24 Egypt 4319 307 1114 2898 25.79% 72 10.29
25 Moldova 3304 94 825 2385 24.97% 49 7.00
26 Kazakhstan 2601 25 646 1930 24.84% 44 6.29
27 Turkey 107773 2706 25582 79485 23.74% 46 6.57
28 Greece 2506 130 577 1799 23.02% 60 8.57
29 Belgium 45325 6917 10417 27991 22.98% 82 11.71
30 Kuwait 2892 19 656 2217 22.68% 62 8.86
31 India 26283 825 5939 19519 22.60% 87 12.43
32 Pakistan 12723 269 2866 9588 22.53% 60 8.57
33 Colombia 5142 233 1067 3842 20.75% 51 7.29
34 United Arab Emirates 9813 71 1887 7855 19.23% 88 12.57
35 Poland 11273 524 2126 8623 18.86% 53 7.57
36 Hungary 2443 262 458 1723 18.75% 53 7.57
37 Belarus 9590 67 1573 7950 16.40% 58 8.29
38 Dominican Republic 5926 273 822 4831 13.87% 56 8.00
39 Morocco 3897 159 537 3201 13.78% 55 7.86
40 Saudi Arabia 16299 136 2215 13948 13.59% 55 7.86
41 Serbia 6630 125 870 5635 13.12% 51 7.29
42 UK 6668 303 857 5508 12.85% 86 12.29
43 Japan 13231 360 1656 11215 12.52% 95 13.57
44 Indonesia 8607 720 1042 6845 12.11% 55 7.86
45 Philippines 7294 494 792 6008 10.86% 87 12.43
46 US 939634 53786 101141 784707 10.76% 95 13.57
47 Qatar 9358 10 929 8419 9.93% 57 8.14
48 Ukraine 8125 201 782 7142 9.62% 54 7.71
49 Russia 74588 681 6250 67657 8.38% 86 12.29
50 France 35456 1456 2949 31051 8.32% 93 13.29
51 Singapore 12693 12 1002 11679 7.89% 94 13.43
52 Panama 5538 159 338 5041 6.10% 47 6.71
53 Ecuador 22719 576 1366 20777 6.01% 56 8.00
54 Sweden 18177 2192 1005 14980 5.53% 86 12.29
55 Portugal 23392 880 1277 21235 5.46% 55 7.86
56 Netherlands 3825 151 104 3570 2.72% 59 8.43
57 Bangladesh 4998 140 113 4745 2.26% 49 7.00
58 Norway 7499 201 32 7266 0.43% 60 8.57
59 Canada 46357 2565 14 43778 0.03% 91 13.00

Mortality Rate by Country

In [20]:
dfww_deaths['Mortality Rate'] = round((dfww_deaths['Deaths'] / dfww_deaths['Confirmed']), 4)

dfww_deaths = dfww_deaths[['Country/Region', 
                                 'Confirmed', 'Deaths', 'Recovered', 
                                 'Active', 'Mortality Rate', 
                                 'Days Since 1st Case', 'Weeks Since 1st Case']]

dfww_deaths = dfww_deaths.loc[dfww_deaths['Confirmed'] > 2000, :]

dfww_deaths.sort_values(by='Mortality Rate', ascending=False)\
.reset_index(drop=True).style.background_gradient(cmap='YlOrRd').format({'Confirmed': '{:.0f}', 'Deaths': '{:.0f}',
                                                              'Recovered': '{:.0f}', 'Active': '{:.0f}',
                                                               'Mortality Rate': '{:.02%}', 'Weeks Since 1st Case': '{:.2f}'})
Out[20]:
Country/Region Confirmed Deaths Recovered Active Mortality Rate Days Since 1st Case Weeks Since 1st Case
0 Belgium 45325 6917 10417 27991 15.26% 82 11.71
1 Italy 195351 26384 63120 105847 13.51% 86 12.29
2 Algeria 3256 419 1479 1358 12.87% 61 8.71
3 Sweden 18177 2192 1005 14980 12.06% 86 12.29
4 Hungary 2443 262 458 1723 10.72% 53 7.57
5 Spain 223759 22902 95708 105149 10.24% 85 12.14
6 Mexico 13842 1305 7149 5388 9.43% 58 8.29
7 Indonesia 8607 720 1042 6845 8.37% 55 7.86
8 Egypt 4319 307 1114 2898 7.11% 72 10.29
9 Brazil 59324 4057 29160 26107 6.84% 60 8.57
10 Philippines 7294 494 792 6008 6.77% 87 12.43
11 Iran 89328 5650 68193 15485 6.33% 67 9.57
12 Ireland 18561 1063 9233 8265 5.73% 57 8.14
13 US 939634 53786 101141 784707 5.72% 95 13.57
14 Romania 10635 601 2890 7144 5.65% 60 8.57
15 Mainland China 82827 4632 78225 -30 5.59% 95 13.57
16 Switzerland 28894 1599 21300 5995 5.53% 61 8.71
17 Canada 46357 2565 14 43778 5.53% 91 13.00
18 Greece 2506 130 577 1799 5.19% 60 8.57
19 Argentina 3780 185 1030 2565 4.89% 54 7.71
20 Poland 11273 524 2126 8623 4.65% 53 7.57
21 Dominican Republic 5926 273 822 4831 4.61% 56 8.00
22 UK 6668 303 857 5508 4.54% 86 12.29
23 Colombia 5142 233 1067 3842 4.53% 51 7.29
24 Finland 4475 186 2500 1789 4.16% 88 12.57
25 France 35456 1456 2949 31051 4.11% 93 13.29
26 Morocco 3897 159 537 3201 4.08% 55 7.86
27 Netherlands 3825 151 104 3570 3.95% 59 8.43
28 Portugal 23392 880 1277 21235 3.76% 55 7.86
29 Austria 15148 536 12103 2509 3.54% 61 8.71
30 India 26283 825 5939 19519 3.14% 87 12.43
31 Czech Republic 7352 218 2453 4681 2.97% 56 8.00
32 Panama 5538 159 338 5041 2.87% 47 6.71
33 Moldova 3304 94 825 2385 2.85% 49 7.00
34 Bangladesh 4998 140 113 4745 2.80% 49 7.00
35 Peru 25331 700 7797 16834 2.76% 51 7.29
36 Japan 13231 360 1656 11215 2.72% 95 13.57
37 Norway 7499 201 32 7266 2.68% 60 8.57
38 Croatia 2016 54 1034 928 2.68% 61 8.71
39 Ecuador 22719 576 1366 20777 2.54% 56 8.00
40 Turkey 107773 2706 25582 79485 2.51% 46 6.57
41 Ukraine 8125 201 782 7142 2.47% 54 7.71
42 Luxembourg 3711 85 3088 538 2.29% 57 8.14
43 South Korea 10728 242 8717 1769 2.26% 95 13.57
44 Pakistan 12723 269 2866 9588 2.11% 60 8.57
45 South Africa 4361 86 1473 2802 1.97% 52 7.43
46 Serbia 6630 125 870 5635 1.89% 51 7.29
47 Thailand 2907 51 2547 309 1.75% 95 13.57
48 Malaysia 5742 98 3762 1882 1.71% 92 13.14
49 Chile 12858 181 6746 5931 1.41% 54 7.71
50 Australia 6694 80 5271 1343 1.20% 92 13.14
51 Kazakhstan 2601 25 646 1930 0.96% 44 6.29
52 Russia 74588 681 6250 67657 0.91% 86 12.29
53 Saudi Arabia 16299 136 2215 13948 0.83% 55 7.86
54 United Arab Emirates 9813 71 1887 7855 0.72% 88 12.57
55 Belarus 9590 67 1573 7950 0.70% 58 8.29
56 Kuwait 2892 19 656 2217 0.66% 62 8.86
57 Bahrain 2588 8 1160 1420 0.31% 62 8.86
58 Qatar 9358 10 929 8419 0.11% 57 8.14
59 Singapore 12693 12 1002 11679 0.09% 94 13.43
In [ ]:
 

Country Deep Dive

Italy

In [21]:
italy = covid.loc[covid["Country/Region"] == "Italy", :]
italy = italy.reset_index(drop=True)


#Create DataFrame of days with Confirmed Cases
italy_temp = italy[italy['Confirmed']>0]
italy_temp = italy_temp.reset_index(drop=True)

#Find start date of infections in YYY-DD-MM format
italy_start = italy_temp['Date'].reset_index(drop=True)[0]

#Find todays date in YYYY-DD-MM format
today = dt.datetime.today()

#Days since first infection date
difference = today - italy_start
difference = difference.days
difference

#Find Daily increase in Confirmed, Deaths, and Recovered
it_confirmed_pct_change = round(italy['Confirmed'].pct_change() * 100, 2)
it_death_change = round(italy['Deaths'].pct_change() * 100, 2)
it_recovery_change = round(italy['Recovered'].pct_change() * 100, 2)

#Find Daily Percentage Increase in Confirmed, Deaths, and Recovered
it_confirmed_perday = italy['Confirmed'].diff()
it_death_perday = italy['Deaths'].diff()
it_recovered_perday = italy['Recovered'].diff()


italy_percent_change = pd.DataFrame

italy_percent = pd.DataFrame({
                   "New Confirmed Cases Per Day": it_confirmed_perday,
                   "Confirmed Percent Change" : it_confirmed_pct_change,
                   "New Confirmed Deaths Per Day": it_death_perday,
                   "Death Percent Change" : it_death_change,
                   "New Recovered Cases Per Day": it_recovered_perday,
                   "Recovery Percent Change" : it_recovery_change,
                    })
    
italy_percent.fillna(value=0)
Out[21]:
New Confirmed Cases Per Day Confirmed Percent Change New Confirmed Deaths Per Day Death Percent Change New Recovered Cases Per Day Recovery Percent Change
0 0.0 0.00 0.0 0.00 0.0 0.00
1 0.0 0.00 0.0 0.00 0.0 0.00
2 0.0 0.00 0.0 0.00 0.0 0.00
3 0.0 0.00 0.0 0.00 0.0 0.00
4 0.0 0.00 0.0 0.00 0.0 0.00
... ... ... ... ... ... ...
81 2729.0 1.51 534.0 2.21 2723.0 5.57
82 3370.0 1.83 437.0 1.77 2943.0 5.70
83 2646.0 1.41 464.0 1.85 3033.0 5.56
84 3021.0 1.59 420.0 1.64 2922.0 5.08
85 2357.0 1.22 415.0 1.60 2622.0 4.33

86 rows × 6 columns

In [22]:
#Merge DataSets together into 1 df
italy_df = italy.merge(italy_percent, left_index=True, right_index=True)
italy_df = italy_df.fillna(value=0)
In [23]:
italy_df
Out[23]:
Province/State Country/Region Date Confirmed Deaths Recovered New Confirmed Cases Per Day Confirmed Percent Change New Confirmed Deaths Per Day Death Percent Change New Recovered Cases Per Day Recovery Percent Change
0 0 Italy 2020-01-31 2.0 0.0 0.0 0.0 0.00 0.0 0.00 0.0 0.00
1 0 Italy 2020-02-01 2.0 0.0 0.0 0.0 0.00 0.0 0.00 0.0 0.00
2 0 Italy 2020-02-02 2.0 0.0 0.0 0.0 0.00 0.0 0.00 0.0 0.00
3 0 Italy 2020-02-03 2.0 0.0 0.0 0.0 0.00 0.0 0.00 0.0 0.00
4 0 Italy 2020-02-04 2.0 0.0 0.0 0.0 0.00 0.0 0.00 0.0 0.00
... ... ... ... ... ... ... ... ... ... ... ... ...
81 0 Italy 2020-04-21 183957.0 24648.0 51600.0 2729.0 1.51 534.0 2.21 2723.0 5.57
82 0 Italy 2020-04-22 187327.0 25085.0 54543.0 3370.0 1.83 437.0 1.77 2943.0 5.70
83 0 Italy 2020-04-23 189973.0 25549.0 57576.0 2646.0 1.41 464.0 1.85 3033.0 5.56
84 0 Italy 2020-04-24 192994.0 25969.0 60498.0 3021.0 1.59 420.0 1.64 2922.0 5.08
85 0 Italy 2020-04-25 195351.0 26384.0 63120.0 2357.0 1.22 415.0 1.60 2622.0 4.33

86 rows × 12 columns

Italy - Confirmed Deaths by Day

In [24]:
fig = px.bar(italy_df, x='Date', y='New Confirmed Deaths Per Day',
             hover_data=['Confirmed', 'Deaths', 'Recovered'], color='New Confirmed Deaths Per Day',
             height=400)
fig.show()

Italy - New Confirmed Cases Per Day

In [25]:
fig = px.bar(italy_df, x='Date', y='New Confirmed Cases Per Day',
             hover_data=['Confirmed', 'Deaths', 'Recovered'], color='New Confirmed Cases Per Day',
             height=400)
fig.show()
In [26]:
us = covid.loc[covid["Country/Region"] == "US", :]
us = us.reset_index(drop=True)

us

#Create DataFrame of days with Confirmed Cases
us_temp = us[us['Confirmed']>0]
us_temp = us_temp.reset_index(drop=True)

United States

In [27]:
us = covid.loc[covid["Country/Region"] == "US", :]
us = us.reset_index(drop=True)

#Group by Date and not state
us_df = us.groupby(by="Date").agg('sum').reset_index(drop=False)


#Create DataFrame of days with Confirmed Cases
us_temp = us_df[us_df['Confirmed']>0]
us_temp = us_temp.reset_index(drop=True)

#Find start date of infections in YYY-DD-MM format
us_start = us_temp['Date'].reset_index(drop=True)[0]

#Find todays date in YYYY-DD-MM format
today = dt.datetime.today()

#Days since first infection date
difference = today - us_start
difference = difference.days
difference

#Find Daily increase in Confirmed, Deaths, and Recovered
us_confirmed_pct_change = round(us_df['Confirmed'].pct_change() * 100, 2)
us_death_change = round(us_df['Deaths'].pct_change() * 100, 2)
us_recovery_change = round(us_df['Recovered'].pct_change() * 100, 2)

#Find Daily Percentage Increase in Confirmed, Deaths, and Recovered
us_confirmed_perday = us_df['Confirmed'].diff()
us_death_perday = us_df['Deaths'].diff()
us_recovered_perday = us_df['Recovered'].diff()


us_percent_change = pd.DataFrame

us_percent = pd.DataFrame({
                   "New Confirmed Cases Per Day": us_confirmed_perday,
                   "Confirmed Percent Change" : us_confirmed_pct_change,
                   "New Confirmed Deaths Per Day": us_death_perday,
                   "Death Percent Change" : us_death_change,
                   "New Recovered Cases Per Day": us_recovered_perday,
                   "Recovery Percent Change" : us_recovery_change,
                    })
    
us_percent.fillna(value=0)
Out[27]:
New Confirmed Cases Per Day Confirmed Percent Change New Confirmed Deaths Per Day Death Percent Change New Recovered Cases Per Day Recovery Percent Change
0 0.0 0.00 0.0 0.00 0.0 0.00
1 0.0 0.00 0.0 0.00 0.0 0.00
2 1.0 100.00 0.0 0.00 0.0 0.00
3 0.0 0.00 0.0 0.00 0.0 0.00
4 3.0 150.00 0.0 0.00 0.0 0.00
... ... ... ... ... ... ...
90 27539.0 3.51 2350.0 5.58 2875.0 3.97
91 28355.0 3.49 2178.0 4.90 2162.0 2.87
92 28950.0 3.45 3332.0 7.15 2837.0 3.67
93 36163.0 4.16 1995.0 3.99 18876.0 23.54
94 32821.0 3.63 1806.0 3.48 1293.0 1.31

95 rows × 6 columns

In [28]:
#Merge DataSets together into 1 df
us_df = us_df.merge(us_percent, left_index=True, right_index=True)
us_df = us_df.fillna(value=0)
In [ ]:
 

New Confirmed Cases Per Day

In [29]:
fig = px.bar(us_df, x='Date', y='New Confirmed Cases Per Day',
             hover_data=['Confirmed', 'Deaths', 'Recovered'], color='New Confirmed Cases Per Day',
             height=400)
fig.show()

New Confirmed Deaths Per Day

In [30]:
fig = px.bar(us_df, x='Date', y='New Confirmed Deaths Per Day',
             hover_data=['Confirmed', 'Deaths', 'Recovered'], color='New Confirmed Deaths Per Day',
             height=400)
fig.show()

Recovered Cases Per Day

In [31]:
fig = px.bar(us_df, x='Date', y='New Recovered Cases Per Day',
             hover_data=['Confirmed', 'Deaths', 'Recovered'], color='New Recovered Cases Per Day',
             height=400)
fig.show()

Confirmed Percent Change

In [32]:
fig = px.bar(us_df, x='Date', y='Confirmed Percent Change',
             hover_data=['Confirmed', 'Deaths', 'Recovered'], color='Confirmed Percent Change',
             height=400)
fig.show()
In [33]:
us_df
Out[33]:
Date Confirmed Deaths Recovered New Confirmed Cases Per Day Confirmed Percent Change New Confirmed Deaths Per Day Death Percent Change New Recovered Cases Per Day Recovery Percent Change
0 2020-01-22 1.0 0.0 0.0 0.0 0.00 0.0 0.00 0.0 0.00
1 2020-01-23 1.0 0.0 0.0 0.0 0.00 0.0 0.00 0.0 0.00
2 2020-01-24 2.0 0.0 0.0 1.0 100.00 0.0 0.00 0.0 0.00
3 2020-01-25 2.0 0.0 0.0 0.0 0.00 0.0 0.00 0.0 0.00
4 2020-01-26 5.0 0.0 0.0 3.0 150.00 0.0 0.00 0.0 0.00
... ... ... ... ... ... ... ... ... ... ...
90 2020-04-21 811865.0 44444.0 75204.0 27539.0 3.51 2350.0 5.58 2875.0 3.97
91 2020-04-22 840220.0 46622.0 77366.0 28355.0 3.49 2178.0 4.90 2162.0 2.87
92 2020-04-23 869170.0 49954.0 80203.0 28950.0 3.45 3332.0 7.15 2837.0 3.67
93 2020-04-24 905333.0 51949.0 99079.0 36163.0 4.16 1995.0 3.99 18876.0 23.54
94 2020-04-25 938154.0 53755.0 100372.0 32821.0 3.63 1806.0 3.48 1293.0 1.31

95 rows × 10 columns

In [34]:
fig = px.bar(us_df, x='Date', y='Death Percent Change',
             hover_data=['Confirmed', 'Deaths', 'Recovered'], color='Death Percent Change',
             height=400)
fig.show()
In [35]:
 fig = px.bar(us_df, x='Date', y='Death Percent Change',
             hover_data=['Confirmed', 'Deaths', 'Recovered'], color='Death Percent Change',
             height=400)
fig.show()
In [36]:
covid_2['Date'] = covid_2['Date'].dt.strftime('%Y-%m-%d')

Confirmed Cases by Country

In [37]:
fig = px.area(covid_2, x="Date", y="Confirmed", color="Country", line_group="Country")
fig.show()
In [38]:
fig = px.area(covid_2, x="Date", y="Deaths", color="Country", line_group="Country")
fig.show()
In [39]:
# covid_2['Date'] = covid_2['Date'].dt.strftime('%Y-%m-%d')
In [40]:
#ADD ISO_CODES for Choropleth
input_countries = covid_2['Country']

countries = {}
for country in pycountry.countries:
    countries[country.name] = country.alpha_3

codes = [countries.get(country, 'Unknown code') for country in input_countries]

#turn into dataframe
codes_df = pd.DataFrame(codes)

#concat
covid_iso = pd.concat([covid_2, codes_df], axis=1)

#Clean-up
covid_iso = covid_iso.rename(columns={0: 'iso_code'})

covid_iso.sort_values(by='Confirmed', ascending=False)

covid_iso.loc[covid_iso['Country'] == 'US' , 'iso_code'] = 'USA'
covid_iso.loc[covid_iso['Country'] == 'Mainland China' , 'iso_code'] = 'CHN'
covid_iso.loc[covid_iso['Country'] == 'UK' , 'iso_code'] = 'GBR'
covid_iso.loc[covid_iso['Country'] == 'Russia', 'iso_code'] = 'RUS'
covid_iso.loc[covid_iso['Country'] == 'South Korea', 'iso_code'] = 'KOR'
covid_iso.loc[covid_iso['Country'] == 'Macau', 'iso_code'] = 'MAC'
covid_iso.loc[covid_iso['Country'] == 'Taiwan', 'iso_code'] = 'TWN'
covid_iso.loc[covid_iso['Country'] == 'Venezuela', 'iso_code'] = 'VEN'
covid_iso.loc[covid_iso['Country'] == 'Vietnam', 'iso_code'] = 'VNM'
covid_iso.loc[covid_iso['Country'] == 'Syria', 'iso_code'] = 'SYR'
covid_iso.loc[covid_iso['Country'] == 'Tanzania', 'iso_code'] = 'TZA'
covid_iso.loc[covid_iso['Country'] == 'Kosovo', 'iso_code'] = 'RKS'
covid_iso.loc[covid_iso['Country'] == 'West Bank and Gaza', 'iso_code'] = 'PSE'
covid_iso.loc[covid_iso['Country'] == 'Iran', 'iso_code'] = 'IRN'
In [41]:
covid_iso.sort_values(by='Confirmed', ascending=False)


try:
    covid_iso['Mortality Rate'] = round(covid_iso['Deaths'] / covid_iso['Confirmed'], 2) *100
except ZeroDivisionError:
    covid_iso['Mortality Rate'] = 0
    

covid_iso[covid_iso['iso_code'] == 'Unknown code']
Out[41]:
Date Country Confirmed Deaths Recovered iso_code Mortality Rate
69 2020-01-27 Ivory Coast 1.0 0.0 0.0 Unknown code 0.0
348 2020-02-07 Others 61.0 0.0 0.0 Unknown code 0.0
377 2020-02-08 Others 61.0 0.0 0.0 Unknown code 0.0
406 2020-02-09 Others 64.0 0.0 0.0 Unknown code 0.0
435 2020-02-10 Others 135.0 0.0 0.0 Unknown code 0.0
... ... ... ... ... ... ... ...
10051 2020-04-25 Holy See 9.0 0.0 2.0 Unknown code 0.0
10063 2020-04-25 Ivory Coast 1077.0 14.0 419.0 Unknown code 1.0
10072 2020-04-25 Laos 19.0 0.0 7.0 Unknown code 0.0
10080 2020-04-25 MS Zaandam 9.0 2.0 0.0 Unknown code 22.0
10092 2020-04-25 Moldova 3304.0 94.0 825.0 Unknown code 3.0

619 rows × 7 columns

Confirmed Numbers by Country TimeLapse

In [42]:
fig = px.choropleth(covid_iso, locations="iso_code", color="Confirmed", hover_name="Country", animation_frame="Date", range_color=[0,150000])
fig.show()

Mortality Rate by Country TimeLapse

In [43]:
fig = px.choropleth(covid_iso, locations="iso_code", color="Mortality Rate", hover_name="Country", animation_frame="Date", range_color=[0,8])
fig.show()

United States by State Data

In [44]:
usa = covid[covid['Country/Region'] == 'US'].reset_index(drop=True)
usa = usa.rename(columns={'Province/State': 'State', 
                          'Country/Region': 'Country'})

usa
Out[44]:
State Country Date Confirmed Deaths Recovered
0 Washington US 2020-01-22 1.0 0.0 0.0
1 Washington US 2020-01-23 1.0 0.0 0.0
2 Washington US 2020-01-24 1.0 0.0 0.0
3 Chicago US 2020-01-24 1.0 0.0 0.0
4 Washington US 2020-01-25 1.0 0.0 0.0
... ... ... ... ... ... ...
3593 Virginia US 2020-04-25 12366.0 437.0 0.0
3594 Washington US 2020-04-25 13319.0 737.0 0.0
3595 West Virginia US 2020-04-25 1010.0 32.0 0.0
3596 Wisconsin US 2020-04-25 5687.0 266.0 0.0
3597 Wyoming US 2020-04-25 491.0 7.0 0.0

3598 rows × 6 columns

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